Abstract

Big data analytics in the Internet of Things (IoT) realm demands a substantial volume of data for training models and making reliable inferences. In most cases, data availability is scarce, and synthetic data is generated from real-world data to meet the needs. Yet, there remains a risk of exposing private and sensitive information without proper data security measures. In this article, we aim to develop a secure collaborative model learning methodology trained on synthetic data, ensuring data availability, privacy and confidentiality through differential privacy and key management. Additionally, we propose a secured inference framework where user data, sent for inference to the deployed model is protected, preserving both the accuracy of the predicted data and the security of the input data. Our experimental evaluation, along with performance and security analysis, exhibits that our approach offers accuracy and scalability while maintaining privacy and security.

Abstract

—With the rise of Big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in Big Data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current Big Data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.

Abstract

Research over several decades has demonstrated modest and moderately consistent relationships between personality and music preference, but has primarily relied on self-reported data and lab-based listening experiments. Recently, music preference research has begun to take advantage of online listening data from platforms such as Spotify and Last.fm to more directly link listening behavior to individual differences. This study extends this new line of research by investigating the associations between naturally occurring music listening behaviors and personality traits, utilizing listening data acquired from Last.fm. We examined social-tagging data by extracting tags related to musical genre and emotion from frequently listened tracks of each participant and clustered them into broader categories to study their association with OCEAN traits. We further evaluated preferences in terms of co-occurring genre and emotion tags and analyzed listening patterns over time to better understand natural patterns of listening as they relate to personality. Our results corroborated previous research, demonstrating a positive correlation between Extraversion and high-arousal music genres such as hip-hop and rap, while Openness was linked with jazz sub-genres. Several novel and nuanced associations were unveiled, such as Extraversion being associated with nostalgic hip-hop and jazz, and Neuroticism correlating with calming music from unconventional genres like shoegaze. Finally, we comment on existing music preference frameworks and their relevance to naturally occurring music listening behavior.

Abstract

The people with Major Depressive Disorder (MDD) exhibit the symptoms of tonal variations in their speech compared to the healthy counterparts. However, these tonal variations not only confine to the state of MDD but also on the language, which has unique tonal patterns. This work analyzes automatic speech-based depression detection across two languages, English and Malayalam, which exhibits distinctive prosodic and phonemic characteristics. We propose an approach that utilizes speech data collected along with self-reported labels from participants reading sentences from IViE corpus, in both English and Malayalam. The IViE corpus consists of five sets of sentences: simple sentences, WH-questions, questions without morphosyntactic markers, inversion questions and coordinations, that can naturally prompt speakers to speak in different tonal patterns. Convolutional Neural Networks (CNNs) are employed for detecting depression from speech. The CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages. The model's performance is evaluated on the collected dataset containing recordings from both depressed and non-depressed speakers, analyzing its effectiveness in detecting depression across the two languages. Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems, thereby enhancing accessibility for diverse populations.

Abstract

Mental health conditions such as depression and anxiety are pervasive issues that affect millions of individuals worldwide. Understanding the intricate relationship between emotional perception and mental health is crucial. This work develops a database named IIITSaint-EmoMDB containing Malayalam language speech samples annotated with emotion ratings at valence and arousal scale using three annotators considering four emotion labels: happy, sad, angry and neutral. In addition to emotion ratings, emotional perception is rated from 150 participants according to the circumplex model of va-lence and arousal. The database also consists of self-reported mental health scores of those participants based on PHQ-9 and GAD-7 questionnaire. Our preliminary analysis of comparing the participants' emotional perception based PHQ-9 and GAD-7 scores revealed that the depressed category showed the highest improvement.

Abstract

Depression, a pervasive mental health issue, highlights the critical need for early detection and effective intervention. A database (InStant-EMDB) is developed to analyze varia tions in the spoken responses of individuals with depression compared to healthy counterparts. Spoken responses are ob tained from English and Malayalam bilingual speakers who respond spontaneously to a set of 15 emotionally evocative words. These words are sourced from the Affective Norms for English Words (ANEW) dataset, which includes valence and arousal ratings for each word. The speech in both English and Malayalam is manually transcribed to accurately reflect the spoken content. The dataset also contains self-reported af fective ratings and data from a mental health survey (PHQ-9) collected from the participants to determine their mental state, which we considered the self-reported depression labels. A preliminary analysis is conducted on the collected speech us ing current state-of-the-art deep learning models such as Con volutional Neural Networks (CNN), Long Short-Term Mem ory networks (LSTM) and Bi-directional Long Short-Term Memory networks (Bi-LSTM). Although distinct linguistic patterns exhibited by individuals struggling with depression are successfully identified by all three models in both Malay alam and English spoken responses, the highest accuracy is achieved by the LSTM models. Our findings and dataset em phasize the potential of linguistic patterns as valuable cues for the early identification and intervention of depression and could contribute to enhancing accessibility for diverse popu lations.

Abstract

In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe behaviour. However, designing or evaluating such a cost function can be prohibitively expensive. For instance, in the domain of self-driving, designing a cost function that encompasses all unsafe behaviours (eg, aggressive lane changes, risky overtakes) is inherently complex, it must also consider all the actors present in the scene making it expensive to evaluate. In such scenarios, the cost function can be learned from feedback collected offline in between training rounds. This feedback can be system generated or elicited from a human observing the training process. Previous approaches have not been able to scale to complex environments and are constrained to receiving feedback at the state level which can be expensive to collect. To this end, we introduce an approach that scales to more complex domains and extends beyond state-level feedback, thus, reducing the burden on the evaluator. Inferring the cost function in such settings poses challenges, particularly in assigning credit to individual states based on trajectory-level feedback. To address this, we propose a surrogate objective that transforms the problem into a state-level supervised classification task with noisy labels, which can be solved efficiently. Additionally, it is often infeasible to collect feedback for every trajectory generated by the agent, hence, two fundamental questions arise:(1) Which trajectories should be presented to the human? and (2) How many trajectories are necessary for effective learning?

Abstract

Preserving the privacy of preferences (or rewards) of a sequential decision-making agent when decisions are observable is crucial in many physical and cybersecurity domains. For instance, in wildlife monitoring, forest rangers must conduct surveillance without revealing animal locations to poachers. This paper addresses privacy preservation in planning over a sequence of actions in MDPs, where the reward function represents the preference structure to be protected. Observers can use Inverse RL (IRL) to learn these preferences, making this a challenging task. Current research on Differential Privacy (DP) in this setting fails to ensure a lower bound on the minimum expected reward and offers theoretical guarantees that are inadequate against IRL-based observers. To bridge this gap, we propose a novel approach rooted in the theory of deception. Deception includes two models: dissimulation (hiding the truth) and simulation (showing the wrong). As our first contribution, we theoretically demonstrate a significant privacy leak in the current dissimulation-based method. Our second contribution is a novel RL-based planning algorithm that uses simulation to effectively address these privacy concerns while ensuring a guarantee on the expected reward. Through experimentation on multiple benchmark problems, we show that our proposed approach outperforms existing methods in preserving the privacy of reward functions.

Abstract

Bell non-locality and steering are archetypal characteristics of quantum mechanics that mark a significant departure from conventional classical notions. They basically refer to the presence of quantum correlations between separated systems which violate a non-local inequality, the violation otherwise is not possible if we restrict ourselves only to classical correlations. In view of the importance of such unique correlations, one may be interested to generate more states exhibiting non-locality starting from a few, a protocol which is termed as broadcasting. However, in the present submission, we show using universal Buzek–Hillary (BH) quantum cloning machine that, if one restricts to broadcasting through local quantum cloning, then such non-locality cannot be broadcasted. Our study is done in the purview of the Bell-CHSH inequality and the CJWR [E G Cavalcanti et al, Phys. Rev. A 80, 032112 (2009)] steering inequality. It is observed that when the number of measurement settings is greater than 6, some of the output states are steerable after the application of local optimal BH cloners. We found that, under some restrictions, if the Werner and Bell diagonal states are subjected to such procedures, then the resultant state is rendered unsteerable. We extended this study to three qubit systems and found that genuine tripartite non-locality cannot be broadcasted using universal BH local quantum cloners, when the Svetlichny’s inequality was considered. For two qubit systems, we considered simulated general local unitaries over Werner and Bell-diagonal states and found that for none of these states, broadcasting on non-locality and 3-steerability is possible.

Abstract

We investigate the phenomenon of transition to synchronization in the Sakaguchi-Kuramoto model in the presence of higher-order interactions and global order parameter adaptation. The investigation is done by performing extensive numerical simulations and low-dimensional modeling of the system. Numerical simulations of the full system show both continuous (second-order) as well as discontinuous transitions. The discontinuous transitions can either be associated with explosive (first-order) or tiered synchronization states depending on the choice of parameters. To develop an in depth understanding of the transition scenario in the parameter space we derive a reduced order model (ROM) using the Ott-Antonsen ansatz, the results of which closely match with those of the numerical simulations of the full system. The simplicity and analytical accessibility of the ROM help to conveniently unfold the transition scenario in the system having complex dependence on the parameters. Simultaneous analysis of the full system and the ROM clearly identifies the regions of the parameter space exhibiting different types of transitions. It is observed that the second-order transition is connected with a supercritical pitchfork bifurcation (PB) of the ROM. On the other hand, the discontinuous tiered transition is associated with multiple saddle-node (SN) bifurcations along with a supercritical PB and the first-order transition involves a subcritical PB alongside a SN bifurcation. Finally, the stability analysis of the different synchronization states of the system is performed analytically.