Abstract
Driven by the NIST's post-quantum standardization efforts and the selection of Kyber as a lattice-based Key-Encapsulation Mechanism (KEM), severalPasswordAuthenticated KeyExchange (PAKE) protocols have been recently proposed that leverage a KEM to create an efficient, easy-to-implement and secure PAKE. In two recent works, Beguinet et al. (ACNS 2023) and Pan and Zeng (ASIACRYPT 2023) proposed generic compilers that transform KEM into PAKE, relying on an Ideal Cipher (IC) defined over a group. However, although IC on a group is often used in cryptographic protocols, special care must be taken to instantiate such objects in practice, especially when a low-entropy key is used. To address this concern, Dos Santos et al. (EUROCRYPT 2023) proposed a relaxation of the ICmodel under the Universal Composability (UC) framework called Half-Ideal Cipher (HIC). They demonstrate how to construct a UC-secure PAKE protocol, EKE-KEM, from a KEM and a modified 2round Feistel construction called m2F. Remarkably, the m2F sidesteps the use of an IC over a group, and instead employs an IC defined over a fixed-length bitstring domain, which is easier to instantiate. In this paper, we introduce a novel PAKE protocol called CHIC that improves the communication and computation efficiency of EKE-KEM, by avoiding the HIC abstraction. Instead, we split the KEM public key in two parts and use the m2F directly, without further randomization. We provide a detailed proof of the security of CHIC and establish precise security requirements for the underlying KEM, including one-wayness and anonymity of ciphertexts, and uniformity of public keys. Our findings extend to general KEM-based EKE-style protocols and show that a passively secure KEM is not sufficient. In this respect, our results align with those of Pan and Zeng (ASIACRYPT 2023), but contradict the analyses of KEM-to-PAKE compilers by Beguinet et al. (ACNS 2023) and Dos Santos et al. (EUROCRYPT 2023). Finally, we provide an implementation of CHIC, highlighting its minimal overhead compared to the underlying KEM - Kyber. An interesting aspect of the implementation is that we reuse the rejection sampling procedure in Kyber reference code to address the challenge of hashing onto the public key space. As of now, to the best of our knowledge, CHIC stands as the most efficient PAKE protocol from black-box KEM that offers rigorously proven UC security.

Abstract
Here we define a new unification algorithm for terms interpreted in semantic domains denoted by a subclass of regular types here called deterministic regular types. This reflects our intention not to handle the semantic universe as a homogeneous collection of values, but instead, to partition it in a way that is similar to data types in programming languages. We first define the new unification algorithm which is based on constraint generation and constraint solving, and then prove its main properties: termination, soundness, and completeness with respect to the semantics. Finally, we discuss how to apply this algorithm to a dynamically typed version of Prolog.

Abstract
Stroke, a vascular disorder affecting the nervous system, is the third-leading cause of death and disability combined worldwide. One in every four people aged 25 and older will face the consequences of this condition, which typically causes loss of limb function, among other disabilities. The proposed review analyzes the mechanisms of stroke and their influence on the disease outcome, highlighting the critical role of rehabilitation in promoting recovery of the upper limb (UL) and enhancing the quality of life of stroke survivors. Common outcome measures and the specific targeted UL features are described, along with emerging supplementary therapies found in the literature. Stroke survivors often develop compensatory strategies to cope with limitations in UL function, which must be detected and corrected during rehabilitation to facilitate long-term recovery. Recent research on the automated detection of compensatory movements has explored pressure, wearable, marker-based motion capture systems, and vision sensors. Although current approaches have certain limitations, they establish a strong foundation for future innovations in post-stroke UL rehabilitation, promoting a more effective recovery.

Abstract
The textile industry is experiencing rapid advancement, reflected in the adoption of innovative and efficient manufacturing techniques. The automation of clothing sewing systems has the potential to reduce the allocation of repetitive tasks to operators, freeing them for more value-added operations. There are several machines on the market that automatically sew the bottom hem of T-shirts, a key component of the garment that fulfills both functional and aesthetic purposes. However, most of them require the fabric to be positioned manually by an operator. To address this issue, this work presents a solution to automate the process of feeding a T-shirt into a SiRUBA sewing machine using a YuMi dual-arm robot. In this scenario, the T-shirt arrives at the workstation with the main front and back pieces of cloth sewn together, seams facing out, and with no sleeves yet. This setup starts by turning the garment inside out with the aid of an automated hanger, ensuring that the seams are facing inward (as the machine requires), and then using the dual-arm robot to feed the garment into the sewing machine. With our approach, the feeding and hemming process took less than 35 seconds, with a feeding success rate of 98%. Therefore, this work can serve as a steppingstone towards more efficient automated sewing systems within the garment production industry.

Abstract

Abstract
Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the annotation of possible events, that may be under represented. From the predominant strategies, weakly and semi-supervised, the former has demonstrated potential to achieve a higher score in its analysis, adding to its flexibility. This work shows that using temporal ranking constraints for Multiple Instance Learning can increase the performance of these models, allowing the focus on the most informative instances. Moreover, the results suggest that altering the ranking process to include information about adjacent instances generates best-performing models.