dataesr/scientific-paragraphs-categorization huggingface dataset:
A second independent cohort of 37 RCC patients, never used during the training of the models, nor for the hyper-parameters optimization phase, were randomly selected to act as the test set for performance evaluation in terms of patient-level predictions. As Figure  2 (c) suggests, for both the canonical CNNs and for our ExpertDT, the final classification at patient-level comes from a majority voting among the predicted tiles associated to the same subject. For all the different CNN models exploited in the RCC subtyping task (VGG16 26 , ResNet50 27 , DenseNet121  28  ), and their corresponding training paradigms (training from scratch, transfer learning from ImageNet, transfer learning from CRC dataset  31  ), we leveraged a grid search based on the KerasTuner package  32  to look for the optimal configuration of the following hyper-parameters: the layer from which the fine-tuning starts (when transfer learning is employed), the learning rate and the optimizer type.
ExpertDeepTree's training As it can be gathered from Figure  2 (b), the backbone of our ExpertDT consists of binary CNN classifiers (grey trapezoids in Figure  2 (b)) arranged in a tree-style architecture, which directly stems from the pathologist's experience, and thus is responsible for the introduction of expert-based knowledge in our DL system. Each binary CNN is individually trained on a reduced subset of the training dataset showing only the two labels of interest for the given binary classification task, artificially balanced via random minority oversampling  33. Specifically: 1. the Root CNN learns the classification between tumor (T) and not-tumor (NT). The T class includes all the cancer subtypes, while the NT class is made up of tissue identified as not-cancerous by the pathologist (fiber, necrosis and normal renal parenchyma).
TEDIS Milestone steps Phase 1 [2009-2010]: Dedicated to the development of conceptual data model and the application system. TEDIS was implemented in relational MySQL™ database, within an n-tiers architecture web information system, using light-weight client engineering and free-license software: HTML, CSS, JavaScript, Apache-tomcat and Java ™ language. Phase 2 [2011-2017]: TEDIS system was tested in a stand-alone mode by psychiatrists, psychologists, and medical residents at Necker university hospital. Phase 3 [2017 -2019]: Institutional endorsement and support from the regional public health agency in parallel with multi-centers deployment through the Assistance Publique des Hôpitaux de Paris (APHP) Intranet. Phase 4 [2019 -Now] Strengthen applications' protection of patient private information before Internet deployment in mid-2019.
TEDIS data TEDIS ASD' patient inclusion is based on ICD-10' F84 class codes reported in the patient discharge summary. The main diagnosis points to the reason which mobilized the most of the medical, clinical and care resources. F84 class codes, may also be present in one of three associated diagnoses, referenced usually to enrich patient' medical characterization. A selection of patients of interest subsets, whose diagnoses are uncertain at the moment of the assessment and whose discharge summary include results of two out of three gold standard ASD' assessment tests 2  , are included in TEDIS' cohort. The data production workload, followed the chronology of expert centers joining the study. Data quality was ensured by medical residents in phase 2 and in phase 3, then by the clinical research assistant.
Psychiatry and psychology assessment tests results are reported in 1233 (93%) cases. Reported cases are of 874 for ADI-R, 593 for CARS, 534 for ADOS and ADOS-2, wheschler scales : 221 reported cases. There are a mean of 2. 7 Psychiatry and psychology assessment tests for each patient record. Figure  2 , shows the main diagnosis dominance of the ICD-10 class codes F84, characterizing ASD' patients. Intellectual deficiency, attention deficiency-hyperactivity and neurological symptomatology are frequently present in ASD' symptomatology. TEDIS' cohort patient data specific subsets, already permitted six medical academic research works and publications in neuro-psychiatry. A similar number of methodological indexed articles were published in medical informatics communities. The presentations to the psychiatrics participating in the study cohort of the of data analysis automatic report were encouraging and motivating for pursuing feeding the cohort with the expert information of ASD patients' assessments.
The proposed mechanism based on TTM and CT ensures that trusted IoV devices are identified with high accuracy and verified reliably. The proposed framework is simulated over real-world data set in MATLAB for various performance metrics, such as altered records, accuracy prediction, response time, and utilities of IoV devices. Simulation results show that the proposed framework provides a significant improvement of approximately 87% in comparison to existing (baseline) approaches while analyzing the accuracy, record alteration, and resource utility among the devices in the network.
Secure communication using TTM The vehicular network is represented using a graph where the vertices are the IoV nodes connected using bidirectional edges. Initially, a uniformly distributed trust value (TV) ∈ [0, 1] is randomly chosen for each node. TV increases or decreases depending on their future communication behavior. TV of each node is recorded in a trust selector database by the IoV devices. Each node calculates the TV of neighboring nodes in each level. The TV calculation is updated after receiving the context information from other IoT devices. The TVs of nodes in the current level are calculated using the TVs from the previous level. If a node has more than one predecessor, the maximum of the TVs of its predecessors is chosen to be the TV. An example case is shown in Fig. 2. a) Level 0: The TV of node Q is 0.
Performance analysis Table  3  and Table  4  show the simulation parameters and the predefined values used for evaluation. The data set of real-world San Francisco Yellow Cabs  [16]  is used in our simulation. In the data set, the mobility of 536 taxis was recorded for a month. In our MATLAB simulation environment, 200 IoV devices in a 500 m × 500 m area change their decisions every 60 s. The mobility rate of each IoV device varies between 40-100 km/h based on weather and congestion conditions. Initially, each IoV device is assigned a random trust value that changes depending on TTM. Moreover, the context prediction accuracy of IoV devices is verified using a contract theory. As depicted in Table  4 , the values g 1 , e 1 , e 2 , z 1 , z 2 , l, l ′ , T max , and R max , respectively, represents profit gain, network scaling, latency, factors affecting scaling, latency, weight gain, resource cost, transmission cost, maximum tolerance latency, and transaction fee.
Evaluation metrics The detailed evaluation metrics such as interaction rate, interaction effect, and previous history are the factors that are considered while computing the trust of and prediction of each node. Along with TTM, the trust value depends on the number of devices communicated and affected while transmitting the information. So, the weight values in the simulation are used to compute and analyze the trust value during the establishment and communication process of the network. The unusually more active devices are generally considered malicious devices whose records are simultaneously recorded and stored as the previous history. The system can directly check the database records and the effect of interactions during the prediction and accuracy to reduce the communication among them.
Acknowledgement This research was supported by the  MSIT Korea  under the  NRF Korea  ( NRF-2022R1A2C4001270 ) and the  Innovative Human Resource Development for Local Intellectualization support program  ( IITP-2023-RS-2022-00156360 ) supervised by the  IITP. This work was also supported by the  King Saud University, Riyadh, Saudi Arabia , through the  Researchers Supporting Project  under Grant  RSP2023R18.
g. , by exploiting correlations). It is also important to acknowledge that proxy variables may be considered as personal information under the GDPR, requiring the same level of privacy protection. Thus, as collecting multiple sensitive information (i. e. , multidimensional data) becomes increasingly prevalent in various industries, protecting this information is a legal obligation and an ethical responsibility. Therefore, this paper contributes to an in-depth empirical analysis of how pre-processing multidimensional data with ϵ-LDP affects the fairness and utility in ML binary classification tasks. We evaluated several group fairness metrics  [5, 30] , including disparate impact  [9] , equal opportunity  [21] , and overall accuracy  [12] , on benchmark datasets, namely, Adult  [14] , ACSCoverage  [14] , and LSAC  [40].
Setup of Experiments General setting. For all experiments, we consider the following setting: -Environment. All algorithms are implemented in Python 3 with Numpy  [36] , Numba  [27] , and Multi-Freq-LDPy  [7]  libraries, and run on a local machine with 2. 50GHz Intel Core i9 and 64GB RAM. The codes we develop for all experiments are available in a GitHub repository  [2]. -ML classifier. We used the state-of-the-art  3  LGBM  [26]  as predictor Ŷ. -Encoding. We only use discrete and categorical attributes, which are encoded using OHE or IVE (see Fig. 1 ) and the target is binary, i. e. , Y ∈ {0, 1}. -Training and testing sets. We randomly select 80% as training set and the remaining 20% as testing set. We apply LDP on the training set only. That is, the samples in the testing set are the original samples (i.
e. , no LDP). -Stability. Since LDP protocols, train/test splitting, and ML algorithms are randomized, we report average results over 20 runs. Datasets. Table  2  summarizes all datasets used in our experiments. For ease of reproducibility, we use real-world and open datasets. -Adult. We use 26000 as threshold to binarize the target variable "income" of the reconstructed Adult dataset  [14]. After cleaning, n = 45849 samples are kept. We excluded "capital-gain" and "capital-loss" and used the remaining 10 discrete and categorical attributes. We considered A s = {gender, race, native-country, age} as sensitive attributes for LDP sanitization and A p = gender as the protected attribute for fairness assessment. -ACSCoverage. This dataset 4  is retrieved with the folktables  [14]  Python package and the binary target "PUBCOV" designates whether an individual is covered by public health insurance or not.
We select the year 2018 and the "Texas" state, with n = 98739 samples. We removed "DEAR", "DEYE", "DREM", and "PINCP" and used the remaining 15 discrete and categorical attributes. We considered A s = {DIS, AGEP, SEX, SCHL} as sensitive attributes for LDP sanitization and A p = DIS as the protected attribute (i. e. , disability) for fairness assessment. -LSAC. This dataset is from the Law School Admissions Council (LSAC) National Bar Passage Study  [40]  and the binary target "pass bar" indicates whether or not a candidate has passed the bar exam. After cleaning, n = 20427 samples are kept. We only consider as attributes: 'gender', 'race', 'family income', 'full time', 'undergrad GPA score' (discretized to {1. 5, 2. 0,. , 4. 5}), and 'LSAT score' (rounded to the closest integer). The 'race' attribute was binarized to {black, other}.
We set A s = {race, gender, family income, full time} as sensitive attributes for LDP sanitization and A p = race as the protected attribute for fairness assessment. Evaluated methods. The methods we use and compare are: -(Baseline) NonDP. This is our baseline with LGBM trained over original data (i. e. , D = (X, A s , Y )). We searched for the best hyperparameters using Bayesian optimization  [11]  through 100 iterations varying: max depth ∈  [3, 50] , n estimators ∈ [50, 2000], and learning rate ∈ (0. 01, 0. 25); -LDP protocols. We pre-processed Z s = M(A s ) of the training sets using all seven LDP protocols from Section 3. 3 (i. e. , GRR, RAPPOR, OUE, SS, BLH, OLH, and THE) as M. We used the best hyperparameters found for the NonDP model and trained LGBM over D z = (X, Z s , Y ). For all datasets, we set d s to 4.
3  for the OAD metric in which the gap between privileged and unprivileged groups was accentuated (favoring the unprivileged group). More specifically, the NonDP baseline has OAD equal to -0. 17, and after satisfying LDP for both uniform and k-based solutions and using all LDP protocols, the gap between the privileged and unprivileged groups increased to -0. 3. In other words, we start with favoritism towards the unprivileged group (negative value) and this favoritism increased after LDP. Note also that when applying the uniform privacy budget splitting solution (see left-side plots), all fairness metrics were less to LDP than our k-based solution and, thus, returned to the NonDP baseline value in low privacy regimes. With our k-based solution (see right-side plots), all fairness metrics continued to be slightly better for all privacy regimes for the Adult dataset in Fig.
2. For the ACSCoverage dataset, not all fairness metrics returned to the NonDP baseline value and for the LSAC dataset, a similar behavior was noticed for both uniform and k-based solutions. These differences are mainly influenced by the domain size k of the sensitive attributes. For instance, while Adult has sensitive attributes with higher values of k, LSAC has many binary sensitive attributes. Fig. 2 : Fairness metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the left-side and our k-based on the right-side), on the Adult  [14]  dataset. LDP impact on utility. Fig. 3 : Fairness metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i.
e. , uniform on the left-side and our k-based on the right-side), on the ACSCoverage  [14]  dataset. Fig. 6 : Utility metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the left-side and our k-based on the right-side), on the ACSCoverage  [14]  dataset. Fig. 7 : Utility metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the left-side and our k-based on the right-side), on the LSAC  [40]  dataset. the seven LDP protocols, considering both uniform and our k-based privacy budget splitting solutions. From these figures, one can note that, in general, the impact of ϵ-LDP on utility metrics is minor. For instance, for the Adult dataset (Fig.
5 ), only ∼ 1% of utility loss for all metrics is observed. Regarding privacy budget splitting, for the Adult dataset, our k-based solution is more robust to LDP as it only drops in higher privacy regimes (i. e. , smaller ϵ values) than the uniform solution. One main explanation for this behavior is because there is more discrepancy in the domain size k's of the sensitive attributes A s and, consequently, more privacy budget ϵ are allocated to those attributes with high k. For this reason, the uniform solution preserved more utility for the ACSCoverage dataset in Fig. 6 , and both solutions had similar results for the LSAC dataset in Fig. 7  due to sensitive attributes with small domain size k. Summary. We summarize our main findings for the three research questions formulated at the beginning of Section 5.
(RQ2) Our k-based solution consistently led to better fairness than the state-of-the-art uniform solution when there exist sensitive attributes with high domain size k (e. g. , for both Adult and ACSCoverage datasets). Naturally, when all sensitive attributes have a binary domain, our k-based solution is equivalent to the uniform solution. For this reason, both state-of-the-art uniform and our k-based solution led to similar privacy-utilityfairness trade-off for the LSAC dataset (see Figs. 4  and 7 ). Therefore, regarding utility, k-based is better when sensitive attributes have higher domain sizes k, which coincides with real-world data collections. (RQ3) In general, GRR and SS presented the best privacy-utility-fairness trade-off for all three datasets. This is because GRR has only one perturbed output value and because SS is equivalent to GRR when ω = 1, thus, not introducing inconsistencies for a user's profile.
The term inconsistency refers to an user being multiple categories in a given attribute, i. e. , being both woman and man at the same time. In fact, this is precisely what happens with UE protocols that perturb each bit independently or with LH protocols in which many values can hash to the same perturbed value. For this reason, since BLH hashes the input set V → {0, 1}, it consistently presented the worst utility results for all three datasets, and only for ACSCoverage (see Fig. 3 ), it presented slightly better fairness results than all other LDP protocols.
A Additional Experiments To validate our findings that LDP can improve fairness without sacrificing much utility, we conducted an additional series of experiments by considering a dynamic number of sensitive attributes d s. Specifically, for each iteration of the 20 runs (for stability), using a specific dataset such as Adult  [14] , ACSCoverage  [14] , or LSAC  [40] , we randomly determined the number of sensitive attributes 2 ≤ d s ≤ 6, ensuring that the protected attribute A p is always included in A s , i. e. , A p ∈ A s. Similar to Figs. 2 3 4 (LDP impact on fairness) and Figs. 5-7 (LDP impact on utility), Figs. 8-13 illustrate the privacy-fairness-utility trade-offs for the Adult, ACSCoverage, and LSAC dataset, respectively. These figures consider the NonDP baseline and the seven LDP protocols, as well as both the uniform and our k-based privacy budget splitting solutions.
From Figs. 8-13, one can observe that the results follow similar trends as those presented in Section 5. Specifically, the LDP pre-processing positively affects fairness while only having a minor impact on the utility of the ML model. Fig. 8 : Fairness metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the leftside and our k-based on the right-side), on the Adult  [14]  dataset. The number of sensitive attributes 2 ≤ d s ≤ 6 is selected uniformly at random. Fig. 9 : Fairness metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the left-side and our k-based on the right-side), on the ACSCoverage  [14]  dataset. The number of sensitive attributes 2 ≤ d s ≤ 6 is selected uniformly at random.
Fig. 11 : Utility metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the leftside and our k-based on the right-side), on the Adult  [14]  dataset. The number of sensitive attributes 2 ≤ d s ≤ 6 is selected uniformly at random. Fig. 12 : Utility metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the left-side and our k-based on the right-side), on the ACSCoverage  [14]  dataset. The number of sensitive attributes 2 ≤ d s ≤ 6 is selected uniformly at random. Fig. 13 : Utility metrics (y-axis) by varying the privacy guarantees (x-axis), the ϵ-LDP protocol, and the privacy budget splitting solution (i. e. , uniform on the left-side and our k-based on the right-side), on the LSAC  [40]  dataset.
Methodology By using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a systematic review of recently published literature was conducted on recent advancements in multimodal human-robot Interaction  (Page et al. , 2021). The inclusion criteria were: (i) publications indexed in the Web of Science, Scopus, and ProQuest databases; (ii) publication dates between 2008 and 2022; (iii) written in English; (iv) being a review paper or an innovative empirical study; and (v) certain search terms covered. The exclusion criteria were: (i) editorial materials, (ii) conference proceedings, and (iii) books were removed from the research. The Systematic Review Data Repository (SRDR), a software program for the collection, processing, and inspection of data for our systematic review, was employed.
The quality of the specified scholarly sources was evaluated by using the Mixed Method Appraisal Tool. After extracting and analyzing publicly accessible papers as evidence, no institutional ethics approval was required before starting our research (Figure  3 ). Throughout April 2008 and October 2022 (mostly in 2022), a systematic literature review of the Web of Science, ProQuest, and Scopus databases was performed, with search terms including "multimodal human-robot interaction, " "multimodal HRI techniques, " "multimodalities used in HRI, " "speech recognition in HR, " "application of multimodal HRI, " "gesture recognition in HRI, " and "multimodal feedback in HRI. " The search keywords were determined as the most frequently used words or phrases in the researched literature. Because the examined research was published between 2008 and 2022, only 359 publications met the qualifying requirements.
Natural Language Generation (NLG): Natural Language Generation (NLG) is a subfield of Natural language processing (NLP) that enables computers to produce natural language responses to humans. NLG has become an important aspect of human-robot interaction (HRI) due to its ability to allow robots to communicate with humans in a more humanlike manner. However, the process of generating natural language responses involves retrieving and synthesizing relevant information from various data sources, such as open data repositories, domain-specific databases, and knowledge graphs. In addition, the NLG process involves the use of complex algorithms and statistical models to generate natural language responses that are contextually appropriate and grammatically correct. Question answering: NLU and NLG together allows the robot to understand and generate answers to questions, for example "What's the weather today?" Dialogue management: NLP can be used to manage the dialogue between the human and the robot, for example to track the state of the conversation, and allow for more seamless interactions, for example by remembering the context of the previous turns in the conversation and using it to generate appropriate responses.
2. Once the text has been generated, it is passed to a Text-to-Speech (TTS) engine, which uses a set of rules, or a machine learning model, to convert the text into speech. This process involves transforming the written text into a phonetic representation that can be pronounced by the robot. 3. The TTS engine can be tuned to mimic different human voices, genders, and even create a virtual robot voice. For example, many systems make use of widely available TTS engines (e. g. , Acapela, Cereproc, Google TTS), which offer a range of voices in different languages and accents. 4. The generated speech is then output through the robot's speakers, allowing the user to hear the response. The quality of the output speech is dependent on the TTS engine, the quality of the audio hardware, and the environmental conditions in which the robot is operating.