Authors - Kalinka Kaloyanova, Elitza Kaloyanova Abstract - Despite the increasing use of artificial intelligence (AI) in healthcare, clinician adoption of AI tools is still obstructed by algorithmic aversion, which reflects scepticism about the results of AI use. This article examines how hospitals can enhance AI adoption by strengthening AI competencies in physicians and mitigating mistrust through a systematic, data-driven approach. A review of behavioral studies reveals barriers to AI adoption across technical, cognitive, organizational, and ethical domains. A framework is proposed that focuses on integrating individual clinician competencies with structured strategies implemented by hospitals that support workflow and create conditions for continuous learning. The implementation of data-centric strategies, explainable AI tools, competency programs, simulation training, and interprofessional collaboration is recommended to increase trust in AI in medicine, support its ethical use, which ultimately leads to safer healthcare delivery.
Authors - Md Manirul Islam, Umme Salsabil, Md. Mushfiqur Rahman, Sazzad Hossain Abstract - This paper presents a compact identity-verification architecture for private web and Internet of Things (IoT) deployments that require tamper evidence without the operational overhead of a full blockchain. The framework separates credential verification from profile-integrity verification across multiple stores: a credential store, a protected-profile store, a reference integrity store, and a key store. Credentials are protected with Argon2id-based verifiers, while protected profile records are bound to entity identifiers, timestamps, and version counters through HMAC-SHA-256 reference tags. Unlike scan-heavy hash-only workflows, the proposed design performs direct indexed lookup by entity identifier and then verifies integrity through a keyed comparison step, improving both security posture and scalability. The same logic can be deployed behind HTTPSbased web services and MQTT-over-TLS IoT gateways. A reference prototype and benchmark study over datasets of 1,000 to 10,000 entities show that the indexed login path remains nearly size-stable, with median successful login latency around 1.68-1.69 ms under a development-profile Argon2id configuration, while a scan-based baseline login path grows from 0.92 ms to 6.90 ms over the same range. Injected profile tampering was detected in all benchmarked trials. The resulting framework offers a pragmatic middle path between conventional centralized login and heavyweight distributed-ledger authorization for institutions that prioritize local autonomy, compartmentalization, and data-integrity assurance.
Authors - MD Junayed Talukdar, Khosro Salmani Abstract - Artificial Intelligence (AI) systems are widespread across fields such as healthcare, finance, employment, and criminal justice, with a substantial impact on the lives of individuals and society. However, AI systems have been shown to perpetuate existing social inequalities, particularly through biases that are not easily discernible. Such biases are embedded in the technical and social structures of AI systems, posing a direct challenge to the principles of Equity, Diversity, and Inclusion (EDI) understood as the acknowledgment of differences among individuals, fairness and equal access, and the valuation of all participants. This study argues that fairness in AI cannot be achieved by focusing solely on technical aspects, necessitating a holistic approach. To investigate this, computational content analysis was applied to 360 occupational narratives generated by ChatGPT across nine professions and four geographic regions (Canada, Germany, India, and Bangladesh) using explicitly gender-neutral prompts. The analysis examined whether AI-generated narratives associate professions predominantly with one gender, and whether such patterns remain consistent across regions. Findings reveal that gender bias persists despite neutral prompting, with male-coded protagonists dominant in technical and manual labor professions and female-coded protagonists dominant in caregiving roles. Although regional conditions influenced the magnitude of gender imbalance, the direction of occupational gender patterns remained largely consistent across all four regions. This study identifies the empirical foundations necessary for future EDI-AI co-design frameworks, outlining the sociotechnical dimensions that such frameworks must address to be effective.
Authors - Yassine Lkhalidi, Mohamed Lkhalidi, Hatim Kharraz Aroussi, Achraf Tifernine Abstract - IoT device authentication remains vulnerable to credential theft and physical-layer impersonation, particularly where resource constraints preclude full PKI deployments. Existing approaches address subsets of this problem: RF fingerprinting exposes templates in plaintext, while zero-knowledge proof (ZKP) schemes authenticate static keys without binding to physical hardware. We propose ZK-RFAuth, a framework integrating Siamese CNN-based RF fingerprinting, Groth16 ZKP embedding verification, and Proof-of-Authority blockchain logging. A device’s hardware imperfections are captured as a compact embedding; a Groth16 circuit proves the L1 distance between a fresh embedding and the registered template falls below a predefined threshold, without revealing either vector. Evaluated on WiSig (28 WiFi transmitters, 224,000 I/Q frames), ZK-RFAuth achieves 91.4% closed-set accuracy, 2.25% Equal Error Rate, and 70.8% rogue rejection at the 95th-percentile operating threshold, requiring only 972 R1CS constraints for 144-byte proofs verified in approximately 3 milliseconds. ZK-RFAuth is the first framework providing physical-layer identity, embedding-level zero-knowledge privacy, open-set rogue detection, and immutable audit logging simultaneously.
Authors - Aryan Sharma, Dipali Baviskar Abstract - Modern computer networks are often equipped with an intrusion detection system (IDS) to detect malicious activities or cyber-attacks. Such a system must have high accuracy on known attacks, and at the same time it must be able to generalise to previously unseen attacks. However, supervised classifiers fail to generalise to new situations because they learn to map input data to output labels under a specific training distribution, and they perform poorly under a different test distribution, which is called distributional shift. In this paper, we propose a gating-based hybrid IDS that combines supervised classifiers with anomaly detectors. The gating network restricts the influence of the anomaly component to the uncertain prediction zone, i.e., the region of the output space where the classifier is uncertain, defined by a probability range of (0.15, 0.75)], and prevents unsupervised noise from affecting the confident supervised decisions. We evaluate the performance of our proposed system on three different test scenarios using the CIC-IDS-2017 dataset. The first test scenario consists of eight known attacks for which we train the classifiers on the corresponding training data, and then we test them on the corresponding test data. The second test scenario is an out-of-distribution stress test, in which we use 99% benign traffic and add DDoS and PortScan attacks to it, and test whether the system is able to detect them. The third test scenario is a zero-day test scenario in which we test the system on a previously unseen SQL Injection attack. Our findings are as follows. First, the Gating Hybrid RF+AE achieves an F1-score of 0.9778 and a precision of 0.9924 on the eight known attacks, which outperforms the standalone RF classifier with an F1-score of 0.9750. Secondly, on the out-of-distribution test scenario, both the RF and GBT classifiers fail to detect the attacks with an F1-score of 0.000, while the Gating Hybrid RF+IF achieves an F1-score of 0.405, which corresponds to a 40.5 percentage-point lift from the F1-score of the anomaly component IF. Thirdly, the Gating Hybrid RF+IF achieves an SQL Injection recall of 47.6% on the zero-day test scenario, while the standalone RF and GBT classifiers achieve an SQL Injection recall of 33.3% on average. All the abovementioned results are supported by 95% Wilson confidence intervals, and we provide root-cause analysis for the extreme results.
Authors - Moises Toapanta T, Jeanette Jordan Buenano, Nancy Jordan Buenano, Maria Cristina Espin Melendez, Pamela Toapanta Pavon, Rocio Llumiquinga A., Dafna Guaman B., Eriannys Gomez D., Pedro Echeverria B. Abstract - The globalization of Information and Communications Technologies (ICTs) the Internet, artificial intelligence (AI), and social media poses serious threats to the integrity, confidentiality, and authenticity of information in higher education institutions (HEIs). The central problem lies in the absence of robust legal frameworks regulating the use of AI, particularly in relation to personal data protection. This study examines perspectives on AI and social media, together with the legal foundations required for the effective administration of HEIs. Using the deductive method and exploratory research, key actors in institutional governance were identified, administrative strengthening indicators were de-fined, and an integrated model was developed to link AI, social media, and regulatory frameworks. It is concluded that improving institutional governance re-quires mitigating the risks associated with the use of these technologies through legal frameworks aligned with national constitutions and regulations. Ecuador, like most Latin American countries, currently lacks such legislation and remains in the analysis phase.
