This paper presents Encrypted Masked Modeling and Gated Fusion (EMF), a privacy-preserving image retrieval framework for JPEG-compliant encrypted images. EMF supports cloud retrieval without plaintext access by extracting syntax-invariant cues from encrypted entropy streams, including variable-length integer categories, block zero counts, run-length profiles, and Huffman symbol statistics. VLI and TZCPB cues are tokenized in parallel and combined through block-level convex gating. DC and AC histogram tokens add global entropy context. Self-supervised pretraining couples Encrypted Masked Modeling with momentum contrastive learning, allowing context-aware prediction of encrypted-domain targets without plaintext reconstruction. For scalable use, EMF adds cancelable indexing through keyed orthonormal rotation, product quantization, and inverted file indexing, with optional Merkle-based verification. Experiments on Corel10K and ImageNet-100 show higher mAP, Precision@100, and Recall@100 than prior encrypted-domain baselines. These results show that fused syntax-invariant cues and cipher-tailored pretraining improve retrieval under encrypted-domain constraints across tested retrieval benchmark settings.

With the advent of Deep Learning-based Artificial Neural Network models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English, and low-resource languages still suffer from a lack of available resources in terms of training datasets as well as models with even baseline evaluation results. Considering the limited availability of resources for low-resource languages, we propose a methodology for adapting self-attentive transformer-based architecture models (mBERT, mT5) for low-resource summarization, supplemented by the construction of a new baseline dataset (76.5k article, summary pairs) in a low-resource language, Urdu. Choosing news (a publicly available source) as the application domain has the potential to make the proposed methodology useful for reproducing in other languages with limited resources. Our adapted summarization model urT5 with up to 44.78% reduction in size as compared to mT5 can capture contextual information of the low-resource language effectively with an evaluation score (up to 46.35 ROUGE-1, 77 BERTScore) on par with state-of-the-art models in the high-resource language of English (PEGASUS: 47.21, BART: 45.14 on XSUM Dataset) . The proposed method provided a baseline approach toward extractive as well as abstractive summarization with competitive evaluation results in a limited resource setup.

Continuous proliferation of hate speech in different languages on social media has drawn significant attention from researchers in the past decade. Detecting hate speech is indispensable irrespective of the scale of use of language, as it inflicts huge harm on society. This work presents a first resource for classifying the severity of hate speech in addition to classifying offensive and hate speech content. Current research mostly limits hate speech classification to its primary categories, such as racism, sexism, and hatred of religions. However, hate speech targeted at different protected characteristics also manifests in different forms and intensities. It is important to understand varying severity levels of hate speech so that the most harmful cases of hate speech may be identified and dealt with earlier than the less harmful ones. In this work, we focus on detecting offensive speech, hate speech, and multiple levels of hate speech in the Urdu language. We investigate three primary target categories of hate speech: religion, racism, and national origin. We further divide these categories into levels based on the severity of hate conveyed. The severity levels are referred to as symbolization , insult , and attribution . A corpus comprising more than 20,000 tweets against the corresponding hate speech categories and severity levels is collected and annotated. A comprehensive experimentation scheme is applied using traditional as well as deep learning–based models to examine their impact on hate speech detection. The highest macro-averaged F-score yielded for detecting offensive speech is 86% while the highest F-scores for detecting hate speech with respect to ethnicity, national origin, and religious affiliation are 80%, 81%, and 72%, respectively. This shows that results are very encouraging and would provide a lead towards further investigation in this domain.

Recommender systems improve browsing experience of users for large amount of items by assisting selection and classification of items utilizing item metadata. The performance of recommender system usually deteriorates when implicit data is used with limited user interaction history also regarded as cold start (CS) problem. This paper proposes a model to address cold start problem using content based technique where user or item metadata is used to break this ice barrier. The proposed method utilizes the feature extraction techniques (such as term frequencyInverse document frequency(TF-IDF)) and word embedding technique (Word2Vec). These content features are then used to predict the ratings for CS items by constructing user profiles using stacked auto-encoder. Experiments performed on largest real world dataset provided by Movielens 20M shows that proposed model outperforms the state-of-the-art approaches in CS item scenario.

Recommender systems are increasingly being used in e-commerce websites to solve the problem of finding right kind of information. Collaborative filtering is considered as most promising method for recommendation because it recommends items based on common interests of users. Trust Aware Recommender Systems (TARS) is an enhancement of traditional recommendation systems to improve recommendation quality which uses trusted users for recommending an item to an active user. From literature, it is proven that including all trusted users in recommendation process reduces its performance so this research work performs a filtration process on users for reduction of trusted neighborhood of an active user. The main idea of this research work is to keep only those users in trusted neighborhood whose rating behavior is similar to an active user. Subspace clustering method is used for filtration process. The proposed algorithm uses both implicit and explicit trust for trust value calculation. The results demonstrates that the proposed algorithm improves results in terms of Mean Absolute Error and Coverage as compared to other conventional methods.

An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre‐processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. A fuzzy logic based technique is proposed for indirect trust computation that measures the credibility of recommendation (rather than credibility of recommender)to determine the influence of each honest recommendation.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. A fuzzy logic based technique is proposed for indirect trust computation that measures the credibility of recommendation (rather than credibility of recommender)to determine the influence of each honest recommendation.

An adaptive trust based security architecture (based on human notion of trust) as a mechanism to secure computing in pervasive environment is proposed. The model encompasses the ability to reason human cognitive behavior and has the capability to adjust in accordance with behavioral pattern changes. This capability is currently lacking in the existing models as they rely only on mathematical calculation. A strategic attack-based analysis is used for evaluation of the proposed model. Finally we have compared our model with existing evolutionary models in this field.

Trust management for multimedia P2P applications in autonomic networking, which is a mathematical evolutionary model and uses historical behaviour for trust calculation is critically analyzed. It is shown that this model is not attack resistant. Several critical remarks on its behaviour are given. An improvement is presented to overcome these shortcomings.

To secure computing in pervasive environment, an adaptive algorithmic approach for access control (based on human notion of trust) is proposed. The proposed model provides support to calculate direct trust in situations when the requesting entity has a past experience with the service. It encompasses the ability to reason human cognitive behavior and has the capability to adjust in accordance with behavioral pattern changes. An adaptive policy based management is proposed to handle strategic malicious behavior. The defense mechanism incorporated by proposed model against strategic attack is also demonstrated. Finally we have compared our model with existing evolutionary models in this field.