Miracle-dz/UniFAR-FTUs huggingface dataset:
Siamese Neural Networks for Content-based Cold-Start Music Recommendation.

Music recommendation systems typically use collaborative filtering to determine which songs to recommend to their users. This mechanism matches a user with listeners that have similar tastes, and uses their listening history to find songs that the user will probably like. The fundamental issue with this approach is that artists already need to have a significant user following to get a fair chance of being recommended. This is known as the music cold-start problem. In this work, we investigate the possibility of making music recommendations based on audio content so that new artists still get a good chance of being recommended, even if they do not have a sufficient number of listeners yet. We propose the use of Siamese Neural Networks (SNNs) to determine the similarity between two audio clips. Each clip is first pre-processed into a Mel-Spectrogram, which is then used as input to an SNN consisting of two identical Convolutional Neural Networks (CNNs). The output of each CNN is then compared together to determine whether two songs are similar or not. These were trained using audio from the Free Music Archive, with the genre used as a heuristic to determine the similarity between song pairs. A query-by-multiple-example (QBME) music recommendation system was developed that makes use of the proposed content-based similarity metric to find songs that match the user’s tastes. This was packaged inside an online blind-test survey, which first prompts participants to select a set of preferred songs, and then recommends a number of songs which the subject is expected to listen to and rate on a Likert scale. The recommendations from the proposed algorithm were stochastically interleaved with songs selected randomly from the preferred genres of the user, as a baseline for comparison. The participants were not aware that the recommendations came from two different algorithms. Our findings show that 60.7% of the 150 participants gave higher ratings to the recommendations made by the proposed SNN-based algorithm. Findings also show that 55% of the recommended songs had less than 1,500 listens, demonstrating that the proposed content-based approach can provide a fairer exposure to all artists based on their music, independent of their fame and popularity.
Comparison Between Video-mediated and Asymmetric 3D Teleconsultation During a Preclinical Scenario

Current teleconsultation solutions for preclinical emergencies can transmit knowledge from a remote expert to a local paramedic using audio and 2D video channels. Such technology lacks precision and efficiency for medical diagnostic tasks, and visual feedback is often missing between participants. We investigate a mixed reality 3D teleconsultation solution for preclinical use, which provides a 3D reconstruction of the local scene to a remote expert, displayed in Virtual Reality. A remote expert can join the local scene virtually as an Augmented Reality avatar. The remote expert can annotate the local scene and guide the local paramedics through the procedure. We explored our system in a user study within a preclinical scenario on a collaborative task of attaching chest lead electrodes of a 12 channel electrocardiogram on a mannequin. We compared the 3D teleconsultation system to a 2D video-mediated teleconsultation via a top-mounted camera and report results from the consultee side in AR. Based on our empirical user study with 10 paramedics with an average of 17 years experience, we observe an improvement in the electrode placement quality using the 3D teleconsultation system. Results indicate no significant difference in the cognitive task-load between conditions. Participants perceived the video-mediated consultation as more usable due to their unfamiliarity with the 3D teleconsultation system. However, participants acknowledge the potential of 3D teleconsultation and believe such a system can significantly improve the preclinical treatment.
Perspectives of Australian family support professionals on the causes and consequences of maladaptive parent-adolescent conflict

ABSTRACT Objectives This study aimed to explore the most prevalent causes and consequences of maladaptive parent-adolescent conflict from the perspectives of Australian psychologists and other family clinicians. We also queried whether these professionals have observed events or times that exacerbate parent-adolescent conflict. Method A cross-sectional survey was used to collect data from one hundred and thirty-seven clinicians working in Australia with parents and adolescents. Data were collected via an author-developed, online survey and frequencies were reported. Results Daily issues of family life (e.g., chores) and relationship readjustment were reported as the most prevalent themes causing parent-adolescent conflict, with problems at school reported as a theme resulting from conflict. Mental health issues in parents and adolescents were reported to (a) be common causes of conflict and (b) worsen after conflict. Trauma and domestic and family violence were identified as additional causes not well represented in existing research. Respondents reported Christmas as a time when conflict was exacerbated. Conclusions Increased awareness amongst clinicians of the likelihood of encountering these issues in their work with families will support competent risk assessment, crisis management and service delivery. Implications of findings for future research on parent-adolescent conflict during specific years of adolescence, training of clinicians and risk management in clinical practice are discussed. KEY POINTS What is already known on this topic: Conflict with parents increases during the adolescent years. Parent-adolescent conflict can be caused by a wide range of factors, and have a range of adaptive and maladaptive consequences. Parents and adolescents experiencing maladaptive conflicts often seek support from family clinicians. What this topic adds: A current exploration of the causes and consequences of parent-adolescent conflict that are most prevalent. The perspectives of the family clinicians who support conflicted parents and adolescents, but have been underrepresented in existing research. Inform early-career clinicians who support families of the most prevalent factors they may encounter in their work, improving risk assessment and crisis management following a conflict.
Attention-Based Deep Recurrent Model for Survival Prediction

Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named AttenSurv, for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named GNNAttenSurv, by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.
Novel Reconfigurable Hardware Systems for Tumor Growth Prediction

An emerging trend in biomedical systems research is the development of models that take full advantage of the increasing available computational power to manage and analyze new biological data as well as to model complex biological processes. Such biomedical models require significant computational resources, since they process and analyze large amounts of data, such as medical image sequences. We present a family of advanced computational models for the prediction of the spatio-temporal evolution of glioma and their novel implementation in state-of-the-art FPGA devices. Glioma is a rapidly evolving type of brain cancer, well known for its aggressive and diffusive behavior. The developed system simulates the glioma tumor growth in the brain tissue, which consists of different anatomic structures, by utilizing MRI slices. The presented models have been proved highly accurate in predicting the growth of the tumor, whereas the developed innovative hardware system, when implemented on a low-end, low-cost FPGA, is up to 85% faster than a high-end server consisting of 20 physical cores (and 40 virtual ones) and more than 28× more energy-efficient than it; the energy efficiency grows up to 50× and the speedup up to 14× if the presented designs are implemented in a high-end FPGA. Moreover, the proposed reconfigurable system, when implemented in a large FPGA, is significantly faster than a high-end GPU (i.e., from 80% and up to 250% faster), for the majority of the models, while it is also significantly better (i.e., from 80% to over 1,600%) in terms of power efficiency, for all the implemented models.
High-Value Token-Blocking: Efficient Blocking Method for Record Linkage

Data integration is an important component of Big Data analytics. One of the key challenges in data integration is record linkage, that is, matching records that represent the same real-world entity. Because of computational costs, methods referred to as blocking are employed as a part of the record linkage pipeline in order to reduce the number of comparisons among records. In the past decade, a range of blocking techniques have been proposed. Real-world applications require approaches that can handle heterogeneous data sources and do not rely on labelled data. We propose high-value token-blocking (HVTB), a simple and efficient approach for blocking that is unsupervised and schema-agnostic, based on a crafted use of Term Frequency-Inverse Document Frequency. We compare HVTB with multiple methods and over a range of datasets, including a novel unstructured dataset composed of titles and abstracts of scientific papers. We thoroughly discuss results in terms of accuracy, use of computational resources, and different characteristics of datasets and records. The simplicity of HVTB yields fast computations and does not harm its accuracy when compared with existing approaches. It is shown to be significantly superior to other methods, suggesting that simpler methods for blocking should be considered before resorting to more sophisticated methods.
Burst-induced Multi-Armed Bandit for Learning Recommendation

In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the audience. To achieve this, we assume that the recommendation procedure can be split into two different states: the loyal and the curious state. We identify the current state by modelling the events as a mixture of two Poisson processes, one for each of the possible states. We further assume that the loyal audience is associated with a single stationary reward distribution, but each bursty period comes with its own reward distribution. We test our algorithm and compare it to several baselines in two strands of experiments: synthetic data simulations and real-world datasets. The results demonstrate that BMAB achieves competitive results when compared to state-of-the-art methods.
Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network

Fashion is more than Paris runways. Fashion is about how people express their interests, identity, mood, and cultural influences. Given an inventory of candidate garments from different categories, how to assemble them together would most improve their fashionability? This question presents an intriguing visual recommendation challenge to automatically create capsule wardrobes. Capsule wardrobe generation is a complex combinatorial problem that requires the understanding of how multiple visual items interact. The generative process often needs fashion experts to manually tease the combinations out, making it hard to scale. We introduce TensorNet, an approach that captures the key ingredients of visual compatibility among tops, bottoms, and shoes. TensorNet aims to provide actionable advice for full-body clothing outfits that mix and match well. Our TensorNet consists of two core modules: a Cross-Attention Message Passing module and a Wide&Deep Tensor Interaction module. As such, TensorNet is able to characterize the local region-based patterns as well as the global compatibility of the entire outfits. Our experimental results on the real-word datasets indicate that the proposed method is capable of learning visual compatibility and outperforms all the baselines. TensorNet opens up opportunities for fashion designers to narrow down the search space for multi-clothes combinations.
Elastic properties of Mg-phase D at high pressure

ABSTRACT The sound velocities of polycrystalline Mg-phase D were investigated at the pressure up to 13.6 GPa and room temperature, using ultrasonic interferometry in a multi-anvil apparatus. The P (VP ) and S (VS ) wave velocities obtained at ambient conditions produced the results of VP = 9.26(3) km/s and VS = 5.46(1) km/s. The results are in accordance with the values for Mg-phase D by Brillouin scattering (VP = 9.25 km/s and VS = 5.51km/s). The results, i.e. Ks0 = 153.8(13) GPa and G0 = 99.8(5) GPa obtained through two-dimensional linear fitting analysis are self-consistent with Ks0 = 153.3(16) GPa and G0 = 99.5(10) GPa derived by third-order Eulerian finite strain equations. We also find that Vp of phase D is 1.8–2.2% lower than Fe-ringwoodite, but Vs is higher than Fe-ringwoodite at the bottom of the mantle transition zone by up to 1.3%.
Cognitive Load During First Contact With Mixed Reality Learning Environments

Mixed Reality (MR) is a growing field in educational settings with increasing importance. Nevertheless, little research has been done so far on how to design a user friendly first contact with this technology. Therefore, this study investigates the challenges and problems that arise when using MR with little to no experience and focuses on the cognitive load of the participants. For this purpose, three tasks are first performed in the natural environment using physical dice, which are then performed by the same participants in MR with virtual dice. Questionnaires are used to assess cognitive load. The problems and challenges are addressed with semi-structured interviews. As a result, a significant increase in cognitive load is observed from the first run (non-MR) to the second run (MR), which is underpinned by the number of challenges encountered. Therefore, further research of the reasons for the increase in cognitive load is needed in order to counteract it with an appropriate design of Mixed Reality Learning Environments.
Correlates of conflict resolution across cultures

Abstract Abstract Conflicts are ubiquitous between individuals as well as between groups. Effective conflict resolution is essential for individual well-being and group functioning and often involves leadership dynamics. The evolutionary human sciences have suggested that conflict resolution is shaped by psychological heuristics, norms and ecology. There are limited empirical data, however, on conflict resolution across cultures. Using a cross-cultural database of 109 leadership dimensions coded from over 1200 text records from the eHRAF ethnographic database, exploratory analyses investigated correlates of conflict resolution. The results revealed greater evidence of conflict resolution among kin groups than political groups and greater evidence of within-group conflict resolution than between-group, which did not vary across subsistence strategies or group contexts, with two exceptions – military group conflicts were biased towards between-group contexts and religious groups biased towards within-group contexts. The strongest predictors of conflict-resolution services were other prosocial functions and included group representation and providing counsel, protection and punishment, as well as qualities of interpersonal skills and fairness. Followers received social service benefits and reduced risk of harm. For leaders who resolve conflicts, status and social benefits were potential negative predictors. These results provide a comparative view of the correlates of conflict resolution suggesting diversity across social contexts.
Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation

A dialog-based interactive recommendation task is where users can express natural-language feedback when interacting with the recommender system. However, the users’ feedback, which takes the form of natural-language critiques about the recommendation at each iteration, can only allow the recommender system to obtain a partial portrayal of the users’ preferences. Indeed, such partial observations of the users’ preferences from their natural-language feedback make it challenging to correctly track the users’ preferences over time, which can result in poor recommendation performances and a less effective satisfaction of the users’ information needs when in presence of limited iterations. Reinforcement learning, in the form of a partially observable Markov decision process (POMDP), can simulate the interactions between a partially observable environment (i.e. a user) and an agent (i.e. a recommender system). To alleviate such a partial observation issue, we propose a novel dialog-based recommendation model, the Estimator-Generator-Evaluator (EGE) model, with Q-learning for POMDP, to effectively incorporate the users’ preferences over time. Specifically, we leverage an Estimator to track and estimate users’ preferences, a Generator to match the estimated preferences with the candidate items to rank the next recommendations, and an Evaluator to judge the quality of the estimated preferences considering the users’ historical feedback. Following previous work, we train our EGE model by using a user simulator which itself is trained to describe the differences between the target users’ preferences and the recommended items in natural language. Thorough and extensive experiments conducted on two recommendation datasets – addressing images of fashion products (namely dresses and shoes) – demonstrate that our proposed EGE model yields significant improvements in comparison to the existing state-of-the-art baseline models.
The importance of nutrition in preventing heat stress at poultry

SUMMARY High production causes birds to be more sensitive to stress. Poultry are exposed to heat stress due to high ambient temperature in summer and being housed in hot regions of the world. Producers suffer economic losses as heat stress negatively affects the optimum productivity of the birds. Some adjustments in the diet can be effective in counteracting the negative effects of heat stress in poultry. Enriching the diet with vitamins E and C, and trace minerals such as selenium and zinc, which benefit antioxidant and immune systems; addition of electrolytes that maintain acid–base homoeostasis; increasing dietary energy, protein, amino acid and calcium levels to prevent nutrient deficiencies are all effective feeding arrangements in reducing/eliminating the negative effects of heat stress in poultry. Consequently, it was observed that nutrient supplementation to diets of poultry exposed to heat stress can reduce the effect of heat stress and increase bird performance, meat quality, egg production, eggshell quality and positively affects the immune and digestive systems.
I Want to Break Free! Recommending Friends from Outside the Echo Chamber

Recommender systems serve as mediators of information consumption and propagation. In this role, these systems have been recently criticized for introducing biases and promoting the creation of echo chambers and filter bubbles, thus lowering the diversity of both content and potential new social relations users are exposed to. Some of these issues are a consequence of the fundamental concepts on which recommender systems are based on. Assumptions like the homophily principle might lead users to content that they already like or friends they already know, which can be naïve in the era of ideological uniformity and fake news. A significant challenge in this context is how to effectively learn the dynamic representations of users based on the content they share and their echo chamber or community interactions to recommend potentially relevant and diverse friends from outside the network of influence of the users’ echo chamber. To address this, we devise FRediECH (a Friend RecommenDer for breakIng Echo CHambers), an echo chamber-aware friend recommendation approach that learns users and echo chamber representations from the shared content and past users’ and communities’ interactions. Comprehensive evaluations over Twitter data showed that our approach achieved better performance (in terms of relevance and novelty) than state-of-the-art alternatives, validating its effectiveness.
Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?

The MIPS (maximum inner product search), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. It is usual that e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to consider a question: who are interested in the items and how to find them? This paper answers this question by addressing a new problem called reverse maximum inner product search (reverse MIPS). Given a query vector and two sets of vectors (user vectors and item vectors), the problem of reverse MIPS finds a set of user vectors whose inner product with the query vector is the maximum among the query and item vectors. Although the importance of this problem is clear, its straightforward implementation incurs a computationally expensive cost. We therefore propose Simpfer, a simple, fast, and exact algorithm for reverse MIPS. In an offline phase, Simpfer builds a simple index that maintains a lower-bound of the maximum inner product. By exploiting this index, Simpfer judges whether the query vector can have the maximum inner product or not, for a given user vector, in a constant time. Besides, our index enables filtering user vectors, which cannot have the maximum inner product with the query vector, in a batch. We theoretically demonstrate that Simpfer outperforms baselines employing state-of-the-art MIPS techniques. Furthermore, our extensive experiments on real datasets show that Simpfer is about 500–8000 times faster than the baselines.