Reviewerly/exHarmony huggingface dataset:
SentBench: Comprehensive Evaluation of Self-Supervised Sentence Representation with Benchmark Construction

Self-supervised learning has been widely used to learn effective sentence representations. Previous evaluation of sentence representations mainly focuses on the limited combination of tasks and paradigms while failing to evaluate their effectiveness in a wider range of application scenarios. Such divergences prevent us from understanding the limitations of current sentence representations, as well as the connections between learning approaches and downstream applications. In this paper, we propose SentBench, a new comprehensive benchmark to evaluate sentence representations. SentBench covers 12 kinds of tasks and evaluates sentence representations with three types of different downstream application paradigms. Based on SentBench, we re-evaluate several frequently used self-supervised sentence representation learning approaches. Experiments show that SentBench can effectively evaluate sentence representations from multiple perspectives, and the performance on SentBench leads to some novel findings which enlighten future researches.
Exploring Accurate and Generic Simile Knowledge from Pre-trained Language Models

A simile is an important linguistic phenomenon in daily communication and an important task in natural language processing (NLP). In recent years, pre-trained language models (PLMs) have achieved great success in NLP since they learn generic knowledge from a large corpus. However, PLMs still have hallucination problems that they could generate unrealistic or context-unrelated information. In this paper, we aim to explore more accurate simile knowledge from PLMs. To this end, we first fine-tune a single model to perform three main simile tasks (recognition, interpretation, and generation). In this way, the model gains a better understanding of the simile knowledge. However, this understanding may be limited by the distribution of the training data. To explore more generic simile knowledge from PLMs, we further add semantic dependency features in three tasks. The semantic dependency feature serves as a global signal and helps the model learn simile knowledge that can be applied to unseen domains. We test with seen and unseen domains after training. Automatic evaluations demonstrate that our method helps the PLMs to explore more accurate and generic simile knowledge for downstream tasks. Our method of exploring more accurate knowledge is not only useful for simile study but also useful for other NLP tasks leveraging knowledge from PLMs. Our code and data will be released on GitHub.
AutoMeta: An Integrated Car Identification System Based on Blockchain

The automotive industry ranks among the most lucrative markets around the globe. Now that mankind has evolved, everyone needs a car to get around on a regular basis. One can purchase a brand-new automobile from the dealer or can opt for buying a resale automobile. The process is quite streamlined when one purchases a brand-new vehicle. But the scenario is quite difficult when anyone buys a second-hand vehicle. Cases like selling the stolen car, hiding the accident history of the vehicle, and forging the vehicle documents are common. Many times, the dealer or broker acts as a middleman between buyer and seller and takes advantage of buyers’ trust and performs malpractices in the transaction. Thus, the major challenges in buying a second-hand car are verification of the related documents and checking the identity of the owner. To address persistent issues in the second-hand automobile market, in this study, we have discussed the development and building of a blockchain-based system called as AutoMeta. In this system, the Regional Transport Office (RTO) registers vehicles with a unique number also called as permanent registration number (PRN) of the vehicle along with some other information and it is updated to a shared ledger. Along with this, the police is responsible for adding FIRs of any vehicle involved in any malicious activity which can be used by the buyer of the second-hand vehicle as well as the RTO to know the authenticity of the vehicle. The primary benefit of blockchain-based AutoMeta is that it can lessen the likelihood of fraud and document forgery.
Dynamic Compact Planar Embeddings

This paper presents a way to compactly represent dynamic connected planar embeddings, which may contain self loops and multi-edges, in $$4m + o(m)$$ bits, to support basic navigation in $$O(\lg n)$$ time and edge and vertex insertion and deletion in $$O(\lg ^{1+\epsilon }n)$$ time, where n and m are respectively the number of vertices and edges currently in the graph and $$\epsilon $$ is an arbitrary positive constant. Previous works on dynamic succinct planar graphs either consider decremental settings only or are restricted to triangulations where the outer face must be a simple polygon and all inner faces must be triangles. To the best of our knowledge, this paper presents the first representation of dynamic compact connected planar embeddings that supports a full set of dynamic operations without restrictions on the sizes or shapes of the faces.
String Covers of a Tree Revisited

We consider covering labeled trees with a collection of paths with the same string label, called a (string) cover of a tree. This problem was originated by Radoszewski et al. (SPIRE 2021), who show how to compute all covers of a directed rooted labeled tree in $$O(n \log n / \log \log n)$$ time and all covers of an undirected labeled tree in $$O(n^2)$$ time and space, or $$O(n^2 \log n)$$ time and O(n)-space. (Here n denotes the number of nodes of a given tree). We improve those results by proposing a linear time algorithm for reporting all covers of a directed tree, and showing an $$O(n^2)$$ time and O(n)-space algorithm for computing undirected tree covers. Both algorithms assume that labeling characters come from an integer alphabet.
Natural Scene Text Detection in Video with Hybrid Text Augmentation and Fusion-Transferred Learning

We propose a robust approach of region proposal network through graph-based approach and hybrid text augmentation for detecting and recognizing the video text with different languages, fonts, complex background, and natural scene patterns. First, we use diverse regions using region proposal network (RPN) to identify the text that positioned in different location in the video with different sizes and scales. The locations are identified by segmenting the regions with similarity measure through graph-based approach that adds ability to find the correctness of the text locations. Then, along with the text augmentation, the ability of RPN is improved in locating and classifying the text by finding the bounding box coordinates. Second, a classification network though transfer learning from VGG19 is adopted to eradicate the false positives. Finally, we developed the fusion technique to obtain a clean scene text layer and verified the correctness of text by optical character recognition techniques.
Gaze Tracking Using Capsule-Based CNN Architecture for HMI

Gaze tracking is one of the most widely used method for human–machine interface (HMI) applications in varied areas, such as customer behavioral analysis, driver fatigue analysis, and many more. Gaze tracking provides information about the direction, where a person is looking at with the help of videos or images of the face of the person or only their eyes. A capsule network-based architecture has been developed in this work to predict human gaze direction. We have analyzed the developed architecture by investigating the performance for subject-independent as well subject-dependent scenarios.
Decentralized Market for Spices Using Hyperledger

Over the years, the retail industry has undergone vast change regarding efficiency. It has moved on from paper-based systems and has moved on to digitization of the whole system which has worked wonders for the industry. This has allowed those industries to proceed with globalization and has allowed for their reach to be expanded to even the most remote places. However, the limits of simple digitization have become apparent and have now stopped bringing any further improvements in efficiency when talking about the supply chains established. Luckily blockchain technology has been rapidly developing over the years, and fortunately, there is a way it can be used by the retail industry. Blockchain technology can allow for even more efficiency if it is incorporated into the existing systems at a time when development of the current systems has stagnated. This paper gives a clear overview of blockchain technology, evaluates the consequences of its integration with the organizational structure of the retail sector for the spice market, and attempts to find out how business may use in the blockchain industry to increase consumer, boost customer allegiance and wholesale supply chain management. It includes intriguing blockchain technologies as well perspective to previous studies on the effectiveness of supply chains in the retail industry.
Improving Affective Event Classification with Multi-perspective Knowledge Injection

In recent years, many researchers have recognized the importance of associating events with sentiments. Previous approaches focus on generalizing events and extracting sentimental information from a large-scale corpus. However, since context is absent and sentiment is often implicit in the event, these methods are limited in comprehending the semantics of the event and capturing effective sentimental clues. In this work, we propose a novel Multi-perspective Knowledge-injected Interaction Network (MKIN) to fully understand the event and accurately predict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage contexts to provide sufficient semantic information and perform context modeling to capture the semantic relationships between events and contexts. Moreover, we also introduce human emotional feedback and sentiment-related concepts to provide explicit sentimental clues from the perspective of human emotional state and word meaning, filling the reasoning gap in the sentiment prediction process. Experimental results on the gold standard dataset show that our model achieves better performance over the baseline models.
Enhancing Implicit Sentiment Learning via the Incorporation of Part-of-Speech for Aspect-Based Sentiment Analysis

Implicit sentiment modeling in aspect-based sentiment analysis is a challenging problem due to complex expressions and the lack of opinion words in sentences. Recent efforts focusing on implicit sentiment in ABSA mostly leverage the dependency between aspects and pretrain on extra annotated corpora. We argue that linguistic knowledge can be incorporated into the model to better learn implicit sentiment knowledge. In this paper, we propose a PLM-based, linguistically enhanced framework by incorporating Part-of-Speech (POS) for aspect-based sentiment analysis. Specifically, we design an input template for PLMs that focuses on both aspect-related contextualized features and POS-based linguistic features. By aligning with the representations of the tokens and their POS sequences, the introduced knowledge is expected to guide the model in learning implicit sentiment by capturing sentiment-related information. Moreover, we also design an aspect-specific self-supervised contrastive learning strategy to optimize aspect-based contextualized representation construction and assist PLMs in concentrating on target aspects. Experimental results on public benchmarks show that our model can achieve competitive and state-of-the-art performance without introducing extra annotated corpora.
Linear-Time Computation of Generalized Minimal Absent Words for Multiple Strings

A string w is called a minimal absent word (MAW) for a string S if w does not occur as a substring in S and all proper substrings of w occur in S. MAWs are well-studied combinatorial string objects that have potential applications in areas including bioinformatics, musicology, and data compression. In this paper, we generalize the notion of MAWs to a set $$\mathcal {S} = \{S_1, \ldots , S_k\}$$ of multiple strings. We first describe our solution to the case of $$k = 2$$ strings, and show how to compute the set $$\textsf{M}$$ of MAWs in optimal $$O(n + |\textsf{M}|)$$ time and with O(n) working space, where n denotes the total length of the strings in $$\mathcal {S}$$ . We then move on to the general case of $$k > 2$$ strings, and show how to compute the set $$\textsf{M}$$ of MAWs in $$O(n \lceil k / \log n \rceil + |\textsf{M}|)$$ time and with $$O(n (k + \log n))$$ bits of working space, in the word RAM model with machine word size $$\omega = \log n$$ . The latter algorithm runs in optimal $$O(n + |\textsf{M}|)$$ time for $$k = O(\log n)$$ .
XAI-Based Light-Weight CNN-HAR Model Using Random Sampling

Human Activity recognition is an active field of research in the current days with more and more advancements in the IoT technology introducing the culture of gadgetization of electronic goods in the world. Smartphones-based HAR is an efficient way to monitor human activity without the need of any specifically designed assistive tools for applications in healthcare monitoring, patient monitoring, management of elderly people etc. Deep learning algorithms reported better performance in comparison with machine learning algorithms, however they are criticized in aspects of computational requirements. Resource constrained Edge-based implementations demand for the design of light-weight models. Hence, we propose a 2-step HAR pipeline involving Random Sampling-based data pre-processing. The proposed approach uses relatively less data for training and the final model based on convolutional neural networks is light-weight involving a low number of model parameters and achieves an accuracy of 96.5% which is higher than state-of-the-art approaches. Local explanations are generated for the black-box deep learning-based HAR model, thus explaining the model behaviour. Local fidelity of the LIME framework has been used to demonstrate the effectiveness of the proposed 2-step pipeline.
Solid Waste Identification and Classification Method Based on Feature Selection and Hybrid ResNet CNN Models in Smart Environment

Garbage disposal is a significant environmental problem that presents a serious obstacle in practically all industrialized and developing nations. It is the biggest obstacle to ecological harmony and sustainable growth. The idea of a smart city is essential for raising living standards. The Indian government has decided to establish 100 smart cities. An innovative sensor network, cameras, wireless devices, fast networks like 5G, IT infrastructure, and certification of their efficient management are all included in a smart city's technological arsenal. When it comes to rethinking cities for environmental sustainability, waste management is an essential component of city administration. This paper mainly focuses on applying convolutional neural networks (CNNs) models to analyze and classify (segregate) solid waste to provide a problem-solving procedure for solid waste materials and improve the separation process of waste. ResNet-152 and MobileNet V2 models were trained using transfer learning to create a convolutional neural network (CNN) and CNN algorithm from scratch for image categorization. The model achieves an accuracy of 0.97 and a loss of 0.03. Therefore, here the proposed work using CNN models to classify garbage produce better outcomes and effectively minimize losses.
Art Generation Using Speech Emotions

Translation of speech to image directly without text is an interesting and useful topic due to the potential application in computer-aided design, human-to-computer interaction, creation of an art form, etc. So, we have focused on developing a novel deep learning and GANs-based model which will take speech as an input from the user, analyze the emotions associated with it and accordingly generate the artwork which has been demanded by the user which will in turn provide a personalized experience. Concept of convolutional VQGAN is used to explore codebook consisting of context-rich visual parts, and the entire composition is modeled with autoregressive transformer architecture. Concept of CLIP—Contrastive Language Image-Pre-Training—also uses transformers a model which is trained to find which particular caption from a collection of captions will best fit with the given image used in our project. The input speech is classified into eight different emotions using MLP classifier trained of RAVDESS emotional speech audio dataset, and this acts as a base filter for the VQGAN model. Text converted from speech plays an important role in producing the final output image using CLIP model. VQGAN + CLIP model together utilizes both emotions and text to generate more personalized artwork.
Classifier-Free Guidance for Generative Adversarial Networks (GANs)

Classifier-free guidance is a recently discovered technique to enhance the images generated by diffusion models. This paper aims to use the same technique in the context of Generative Adversarial Networks (GANs) and contrast and examine the images produced. Its main focus is determining if classifier-free guidance is unique to diffusion models or if can it be applied to GANs too, and if the answer was found to be affirmative, in what manner. Previous research has shown that classifier-free guidance in diffusion models produces better images with a minimal impact on conditional modeling performance. With a guidance scale above the value of unity, the images shown are better in quality than those produced conditionally or unconditionally. Classifier-free guidance has yielded considerably better results for diffusion models. The images generated after performing classifier-free guidance show promise for generating high-quality images after sufficient training. Some images are already more photorealistic than those produced just conditionally. This shows that classifier-free guidance can be made conducive for other image generation models as well. It opens doors for further research on enhancing the image generation quality of models other than diffusion models as well and helps in making models like GANs successfully tackle the Generative Learning Trilemma.
Prediction of Next Words Using Sequence Generators and Deep Learning Techniques

Would not it be wonderful if your computer could anticipate what you may type as your next word? For a certain user's texting or typing, the next word prediction can be very helpful. People would be more productive as a result of the significant time savings. Predictive text is an input technology that facilitates typing on a mobile device by suggesting words that the end user may wish to insert in a text field. Predictions are based on the context of other words in the message and the first letters typed. We intend to bring this prediction to the next level. Recurrent neural networks are used in prediction of next word which is a neural application. Standard RNNs can be trained to tackle some issues, but it can be challenging to teach them to learn long-term temporal dependencies. LSTM networks are applied to solve this problem. By advancing the technology now in use, we hope to anticipate the next words that could fit the statement and create a user-friendly application for it.
Analyzing the Involvement of Various Similarity Measures in Trust and Distrust-Based Recommender System

Recommender system supports person in suggesting products of interest during billions of relevant objects. Aim to discover users who share similar interests, such as the belief that users who have previously had identical preferences, will do so again in the future. Recommender systems are built to suggest relevant items to the users. Trust and distrust are factor in recommendation system which helps to improve accuracy of the system. Moreover, a trust network provides detail about the trustworthiness of users upon each other. By adding knowledge about distrust worthy users, it improves the effectiveness of the recommender. Combination such as trust and distrust has the potential of offering better personalized recommendation in the user networks. In this work, we are analyzing the trust and distrust of recommendation system using various similarity measures to explore the better similarity approach for the same. Along with that, we are also incorporating trust as well as distrust propagation.
Touch Pointer Movement-Based PIN Entry in Smartphones to Assist Persons with Visual Impairments

Usable security is an area of active research. Several factors needs to be considered while making the security usable. As the digital platforms have evolved into the primary source of information and service consumption, it is essential to make digital space secure and usable for all categories of users which certainly includes persons with visual impairments. Several attacks have been targeted toward people with visual impairments, as they are dependent on the Internet for their day-to-day activities. With the significant number of users with disabilities, there are various attempts to make the digital ecosystem safer for persons with disabilities. This paper discusses the key challenges encountered while designing a usable security systems for persons with visual impairments. This work proposes a shoulder surfing resistant Personal Identification Number (PIN) entry mechanism for visually impaired individuals utilizing smartphone’s touch feature. The proposed model maps the finger movements into a predefined digit. As the movements look random for an outsider, shoulder surfing becomes significantly complex.
Logistics Network Optimization Using Quantum Annealing

Logistics network optimization problems are of practical importance, and they usually are NP-hard combinatorial optimization problems. In this paper, we explore the potential of using quantum annealing for a parcel distribution centre network optimization using data instances of different sizes. Given the current limitation of quantum hardware, we scale down the data and employ distance-based sorting for reducing the search space. Due to the large number of decision variables in the problem, we use D-Wave’s built-in hybrid quantum-classical Kerberos sampler for solving the problem. Then, we use an iterative hybrid approach for improving the quality of solutions obtained from the Kerberos sampler. We compare the Kerberos sampler solutions with the random sampler, greedy heuristic and exact solver and simulated annealing sampler solutions. Improvement in objective value over random has been shown for different approaches over different data instance sizes. Kerberos sampler performs better than greedy heuristic and at par with exact solver for some data instances.