Reviewerly/exHarmony huggingface dataset:
Visual Sentiment Classification of Restaurant Review Images using Deep Convolutional Neural Networks

In the recent years online reviews are prevalent. Over the years people have started giving feedback about a restaurant by posting images as part of a review where the sentiment polarity is classified based on the facial expressions or the foods. Even more to it is a piece of text along with the image that gives more clear understanding about the picture. As there is tremendous work carried over on text sentiment analysis(SA), in this paper we are focusing on visual analysis to identify whether a given image expresses positive or negative sentiment. In this paper, an image sentiment prediction model is built using Convolutional Neural Networks(CNN). The objective of this work is to perform sentiment classification efficiently and enhance the accuracy of restaurant image dataset posted on social media. The results show that the proposed model achieves better performance on analysis of opinions from images compared to naive bayes which is a machine learning technique.
English to Marathi Text Translation using Deep learning

In today’s times, though text translation has become a very easy task, translation to Indian local languages still has some technical issues to deal with. Thus, to avoid such irregularities a text translator is proposed. The work aims to translate text from English to Marathi with an improved efficiency and accuracy. Further, the paper reveals information regarding the role of Deep Learning and Natural Language Processing (NLP) in development of the Model. The input text goes through the process of tokenization. The processed data is sent to the encoder and decoder model where it undergoes through different layers. Embedding layers accept the text in English. The model makes use of Long Short-Term Memory (LSTM) for encoding the sequence of words. The model has testing accuracy of 90.66%.
Efficient Scanning and Packaging of Shipments in Industries

Scanning devices are commonly used in warehouses or POS [Point of Sale terminals] to speed up data entry tasks and to minimize manual errors while recording the entries into the system. Users working with scanning devices to scan materials or packages usually perform the scanning activity in a large set of transactions each workday. The person working with the scanning device needs to do this activity in a repeated manner, continuously and with minimal deviations to optimize the performance output and efficiently manage the workload. Organizations look for ways to further optimize the processing time(s) to move the inventory faster while reducing the number of steps [iterations] to complete the scanning activity for a given set of activities. In this paper, a methodology that is designed and developed to optimize the scanning activity is being highlighted. Furthermore, an efficient way to pack, move the inventory faster, reduce the number of iterations and a performance evaluation is carried out with traditional approaches.
Target Depth Estimation Using A Linear Array of Cameras: Analysis and Experimental Validation

Target depth estimation is an important problem in signal processing, and finds applications in several fields such as robotics and autonomous systems. In this paper, we consider the problem of target depth estimation using a linear array of identical cameras. We derive a simple closed-form expression for calculating the depth of a single target present between any two cameras, in terms of the inter-spacing distance between the cameras, focal length and disparity values. We validate the mathematical development through numerical results. Further, we consider a simple experimental setup with three cameras from smartphones and study the tightness of the derived expression, in terms of the mean-squared error.
Generic Recommendation Engine using Hybrid Filtering Model

Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. Recommendation systems are mainly employed in applications such as online market, which works with big data. Performing data mining on big data is a tedious task due to its distributed nature and enormity. There are humanely overwhelming number of items for us to inspect, evaluate and choose from. This poses a huge challenge, since overwhelming the customers with huge catalog of items out of which the major portion of items are unrelated to user preferences.There is an imminent need for a recommendation system that eases the process of choosing products by the user and thereby enriching the user experience. To overcome this problem, a recommendation system that uses multiple ML algorithms, a hybrid version of content based filtering and collaborative item-item filtering algorithm is implemented so as to achieve better accuracy in recommendations. The project is aimed to result in a generic recommendation engine suitable for using with any type of items irrespective of domain and datasets.
A CNN Based Approach to Classify the Folk Dances of Odisha

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN).
Sign Language Recognition and Translation to Speech for Mine Workers using Deep Learning Technologies

In this paper, we mainly focus on the use of sign language as an optimal means of communication in underground mines. Deep mining takes place in a highly technical and demanding environment, requiring major new solutions and best practices, as well as increased safety regulations, in order to overcome the hurdles and reap significant economic benefits. In this proposed solution, we use the technology of image processing to detect and recognise hand signs and convert them into audio messages which can be communicated to each worker in a wireless and hassle-free manner.
Supply Chain Partner Onboarding using Conversational AI and NLP in B2B Context

Partner onboarding is the most important process in B2B supply chain management. Partner onboarding comprises collecting data from customers, data about customers’ partners, checking the compliance, assessing the risk associated, and integrating them with the supply chain platform of the service provider. It is important because it paves the way to create strong customer relationships, thus improving the outcomes. Often this is a challenge since onboarding involves constant manual inter-actions, between customers, trading partners, and B2B service providers. With the existing process to onboard suppliers, we risk damaging our relationship with the customers and partners. Using AI, the process of onboarding customers becomes easier for B2B service providers with hundreds of customers and thousands of customer trading partners. Conversational AI and chatbots are some of the primary applications of AI. We propose a solution using AI to automate the partner onboarding process. This solution uses state-of-the-art conversational AI and other NLP techniques using which we can automate the onboarding of customers and trading partners, providing real-time project status, multilingual support for onboarding, cost reduction, and round-the-clock availability. This solution can also be used to check the real-time project status and analyze the real-time cost based on the usage of the services. The chatbots are personalized according to the user login and each user will have their version of details to be updated. This solution is deployed on the cloud and is available for the service provider, their customers, and the associated trading partners.
Face De-identification Scheme Using Landmark-Based Inpainting

Due to the spread of various information and communication technologies, a huge amount of images are produced and shared for diverse purposes. Several de-identification techniques for photos, such as pixelation, blur, and mask, are routinely used in light of recent worries about the growing number of privacy leakages. However, due to the low image quality and loss of many facial features, these de-identified images are not suitable for use in applications such as training models that require a lot of high-quality data. Therefore, in this paper, we propose a new face de-identification method focusing only on facial regions essential for personal identification. By generating facial landmarks differently from the original person using masking and generative adversarial networks-based inpainting, our method can perform de-identification efficiently. To demonstrate the performance of our proposed scheme, we conducted quantitative and qualitative evaluations using an open dataset. We show that our proposed scheme outperforms other de-identification methods.
Attention-based Interactions Network for Breast Tumor Classification with Multi-modality Images

Benefiting from the development of medical imaging, the automatic breast image classification has been extensively studied in a variety of breast cancer diagnosis tasks recently. The multi-modality image fusion was helpful to further improve classification performance. However, existing multi-modality fusion methods focused on the fusion of modalities, ignoring the interactions between modalities, which caused the inefficient performance. To address the above issues, we proposed a novel attention-based interactions network for breast tumor classification by using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Specifically, we proposed a multi-modality interaction mechanism, including relational interaction, channel interaction, and discriminative interaction, to design an attention-based interaction module, which enhanced the abilities of inter-modal interactions. Extensive ablation studies have been carried out, which provably affirmed the advantages of each component. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), specificity (SPC), and sensitivity (SEN) were 87.0%, 87.0%, 88.0%, and 86.0%, respectively, also verifying its effectiveness.
Blink recognition using flexible graphene stress sensor and RFID chipless tagging technology

For people with disabilities who are incapacitated, there are few ways to interact with the computer, and using blink to control can be one of the solutions. This paper first converts the contraction signal of the orbicularis oculi muscle into the electrical signal of graphene resistance change during blinking by a graphene flexible sensor, and then extracts the time and frequency domain features of the electrical signal of graphene resistance change during human blinking control, and compares them with the previously established personal blink data database to realize the control command.
Comparison of image pre-processing methods in liver segmentation task

Automatic liver segmentation of Computed Tomography (CT) images is becoming increasingly important. Although there are many publications in this field there is little explanation why certain pre-processing methods were utilised. This paper presents a comparison of the commonly used approach of Hounsfield Units (HU) windowing, histogram equalisation, and a combination of these methods to try to ascertain what are the differences between them and how big the differences are. All experiments were conducted on the LiTS dataset. To achieve comparable and reliable results only one architecture of neural network is used which is U-Net with ResNet34 blocks.
Person Search via Background and Foreground Contrastive Learning

The specific person search is the foundation of a wide range of applications in intelligent security and surveillance systems. Although detection and re-id have been widely studied, they are difficult to apply to practical applications directly. Therefore, this paper focuses on person search, which aims to solve person detection and person re-identification (re-id) jointly. The common practice is to append the standard detection loss and re-id branches parallelly on Faster RCNN. The traditional re-id utilized Online Instance Matching (OIM) to pull a sample closer to its identity class. However, the relationship among RoIs of an image has not been fully explored in previous methods. To address this issue, we propose Background and Foreground Contrastive Loss (BFCL) to further boost re-id performance. We consider that RoIs from one image have a high probability of containing similar patterns, which might disturb the re-id performance. Therefore, we proposed BFCL to strengthen the learning of distinguishing similar background and foreground by leveraging inter-RoIs pairwise similarity. In summary, our method jointly optimizes the regression loss, classification loss, re-id loss, and the proposed BFCL for achieving optimal performances in person search model. Experiments are performed on two large-scale person search datasets, CUHK-SYSU and PRW. Results show that the proposed BFCL consistently boosts the performance of the baseline framework SeqNet in two datasets. The improved results demonstrate the effectiveness of the proposed BFCL and the necessity of exploring the relationship among RoIs.
Systems Dynamics Modeling for Evaluating Socio-Technical Vulnerabilities in Advanced Persistent Threats

The paper focus on the application of Systems Dynamics Modelling (SDM) for simulating socio-technical vulnerabilities of Advanced Persistent Threats (APT) to unravel Human Computer Interaction (HCI) for strategic visibility of threat actors. SDM has been widely applied to analyze nonlinear, complex, and dynamic systems in social sciences and technology. However, its application in the cyber security domain especially APT that involve complex and dynamic human computer interaction is a promising but scant research domain. While HCI deals with the interaction between one or more humans and between one or more computers for greater usability, this same interactive process is exploited by the APT actor. In this respect, using a data breach case study, we applied the socio-technical vulnerabilities classification as a theoretical lens to model socio and technical vulnerabilities on systems dynamics using Vensim software. The variables leading to the breach were identified, entered into Vensim software, and simulated to get the results. The results demonstrated an optimal interactive mix of one or more of the six socio variables and three technical variables leading to the data breach. SDM approach thus provides insights into the dynamics of the threat as well as throw light on the strategies to undertake for minimizing APT risks. This can assist in the reduction of the attack surface and reinforce mitigation efforts (prior to exfiltration) should an APT attack occur. In this paper, we thus propose and validate the application of system dynamics approach for designing a dynamic threat assessment framework for socio-technical vulnerabilities of APT.
Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze

There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for desktop and mobile applications and conjecture how a UI will influence AR users’ performance. In this work, we introduce a predictive model for determining users’ performance for a target UI without the subsequent involvement of participants in user studies. The model is trained on participants’ responses to objective performance measures such as consumed endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large variability in the depth and context of the menus is ensured by randomly and dynamically creating the hierarchical drop-down menus and associated user tasks from words contained in the lexical database WordNet. Subjective performance bias is reduced by incorporating the users’ non-verbal standard performance WAIS-IV during the model training. The semantic information of the menu is encoded using the Universal Sentence Encoder. We present the results of a user study that demonstrates that the proposed predictive model achieves high accuracy in predicting the CE on hierarchical menus of users with various cognitive abilities. To the best of our knowledge, this is the first work on predicting CE in designing UI for immersive AR applications.
Intelligent Voice Assistants in home care: an initial overview on requirements, hopes and fears of professionals

In the scope of co-creation sessions, care workers provided insights on applications and on concerns about Intelligent Voice Assistants (IVA) in the home of their clients or patients. The sessions focused on the potential to support the care documentation process by IVA. Participants’ expectations and worries spanned from the ability to handle dialects, to confidentiality issues, to integration in existing care documentation systems. However, there is a general openness toward the idea to employ IVA as means to improve the quality of care. The challenge foreseen for using IVA is to become as time efficient as care documentation systems in place. Alternatively, as suggested by participants, IVA could complement existing processes or even create new ones in the care context.
A method for distinguishing between involuntary and voluntary blinks based on graphene sensor

In this study, we develop a judging method and device to distinguish between involuntary and voluntary blinks. This method mainly uses graphene sensor, which is attached to the orbicularis oculi muscle, to collect electrical signals of graphene resistance changes, and attains the detection range of the temporal features and peak features of different types of blinks to build a database. On this basis, the temporal, and peak features of the electrical signal of obtained single blink are extracted to complete the recognition of one single blink. The introduced solution allows for stable, reliable, and accurate blink recognition, excluding the influence of the environment.
User Expectations Regarding Design Dimensions of Adapative Assistance Systems

Recent studies on assistance systems show that adaptive support for users is becoming increasingly important, as this is a promising approach to ensure acceptance, learning support, and appropriate assistance in the long term. Developers of assistance systems are therefore faced with the challenge of designing various dimensions of adaptivity in such a way that they are conducive to acceptance and support. In this paper, we consider the different dimensions and options for the adaptation of assembly assistance and present the results of a first survey to investigate users’ expectations regarding the adaptability of assembly assistance systems. Our evaluation shows that users have clear preferences concerning the control over adaptation and adaptation goals, while other aspects of adaptation assistance do not indicate a clear tendency. The results of this survey should serve as a decision support in the design phase of adaptive assistance systems to choose between different implementation options and concepts.
A Dynamic Adjustment System of Vehicle Rearview Mirror using Gaze Detection

Multi-model interaction research in the cockpit has yielded many interactive modes. Yet, the research on visual interaction is primarily focused on vehicle-to-human transmission. By utilizing eye-tracking technology, we propose a novel approach to introduce the driver’s dynamic attention to human-vehicle system interactions. In our proposed system, the angle of the rearview mirror can be dynamically adjusted according to the driver’s Area of Interest (AOI), which is obtained by identifying the driver’s gaze point. In the experimental verification results, our solution is faster than the manual adjustment scheme to adjust the target in the rearview mirror to the center of the area. In real-world applications, this method can solve the blind spots of rearview mirrors that may exist in dynamic driving tasks, especially in the variant task. Moreover, the method can be compatible with the Driver Monitor System (DMS) in the intelligent cockpit, which can be implemented easily and at a low cost.
Dynamics of entanglement in coherent states, entangled SchrÖdinger cat state and distribution function

We exploit the dynamics of entangled system between a harmonic oscillator and a single mode electromagnetic field as two harmonic oscillators, to examine quantum entanglement in coherent states. Through the Schrödinger cat state, we compute analytically its distribution function. Then, we check graphically the evolution of entanglement and the distribution function for two particular cases: the first is defined by two harmonic oscillators in quantum motion in a Paul trap; here whatever the quantum state, the system is at most entangled. The second case is visualised by two harmonic oscillators with a perturbative force, in that case, by varying the displacement numbers of the coherent states, the quantum entanglement and the distribution function present a similar evolution and they reach very large values. Comparing the two models: in the first, the dynamics of quantum entanglement exhibits a multi-frequency lines behaviour while in the second model, it shows an exponential behaviour.
A simple test for the separability of symmetric quantum states

We present a direct test to verify separability of symmetric pure states of n quantum bits, that is, n-qubit states that are invariant under any element of the permutation group.
Framework for Modelling of Learning, Acting and Interacting in Systems

This work outlines unified formal approach to modelling and analysis of different ML problems in single-user and multi-user setting. This is done by extending formal framework of game theory and enriching standard definition of the game by introducing state (or memory) and algorithm for the players. This together creates formal tool allowing expression of the subtle properties of the models and algorithms. Additionally proposed definitions try to capture stochastic aspects of both algorithms and environment algorithms operate hence make stochasticity internal to the model.
Deontic Sentence Classification Using Tree Kernel Classifiers

The aim of this work is to employ Tree Kernel algorithms to classify natural language in the legal domain (i.e. deontic sentences and rules). More precisely, an innovative way of extracting labelled legal data is proposed, which combines the information provided by two famous LegalXML formats: Akoma Ntoso and LegalRuleML. We then applied this method on the European General Data Protection Regulation (GDPR) to train a Tree Kernel classifier on deontic and non-deontic sentences which were reconstructed using Akoma Ntoso, and labelled using the LegalRuleML representation of the GDPR. To prove the non-triviality of the task we reported the results of a stratified baseline classifier on two classification scenarios.