Miracle-dz/UniFAR-FTUs huggingface dataset:
Optional course in data science: Fairness in two-sided recommendation

Fairness in machine learning systems has become a highly active area in the past few years, as the ethical concerns surrounding automated decision-making become more prominent and these systems become more commonplace. Recommendation systems are not the exception: they operate in different contexts, from seemingly innocuous content recommendation to more sensitive areas such as targeted job offers. Although most of the literature on recommendation fairness focuses on whether users receive fair recommendations, in the context of platforms that mediate between said users (or consumers) and producers (or creators) whose livelihood is directly impacted by the recommendation system in place, it becomes crucial to consider two-sided fairness. This project focuses on the specific case study of the videosharing application TikTok and the perceptions of fairness in recommendation from content creators and consumers in the platform. In particular, the aim is to explore insights from online user reviews, not only from TikTok but also from Youtube, another prominent videosharing application and website; the idea is to gain a broader overview with this comparative study format. While previous work has displayed effectiveness in eliciting valuable and actionable user perspectives from online reviews, there are particular challenges when an elaborate predefined topic such as two-sided recommendation is the focus a priori as in this project. Moreover, the considerable volume of reviews for applications as popular as TikTok and Youtube is another prominent challenge, notably regarding the constraints it implies for performing manual analyses of the data. In this work data analysis was approached first through given metrics in the reviews themselves, i.e. user scores and “thumbs up” or “upvotes” for the reviews themselves. Afterwards more elaborate techniques as sentiment analysis and topic modelling were introduced to gain further insights: sentiment analysis (and emotion classification) is useful to expound on user scores, as the content of the reviews might not align consistently with the provided score. Statistical topic modelling was performed to investigate potential themes present in the data, and manual analysis for some selected reviews was also performed. As discussed ahead, this was helpful in revealing the main problems when eliciting users’ perception of fairness in the thematically unstructured setting of self-motivated reviews, and most importantly, the analysis reveals how, under some considerations, TikTok users seem to exhibit a greater preoccupation with recommendation
IoT-based AQI Estimation using Image Processing and Learning Methods

Air pollution is a concern to the health of all living beings. It is essential to check on the quality of air in the surroundings. This article presents an IoT-based real-time air quality index (AQI) estimation technique using images and weather sensors on Indian rods. A mixture of image features, i.e., traffic density, visibility, and sensor features, i.e., temperature and humidity, were used to predict the AQI. Object detection and localization-based Deep Learning (DL) method along with image processing techniques were used to extract image features while an Machine Learning (ML) model was trained on those features to estimate the AQI. In order to conduct this experiment, a dataset containing 5048 images along with co-located AQI values across different seasons was collected by driving on the roads of Hyderabad city in India. The experimental results report an overall accuracy of 82% for AQI prediction.
Soil pH and Soluble Organic Matter Shifts Exerted by Heating Affect Microbial Response

Fire-induced alterations to soil pH and organic matter play an important role in the post-fire microbial response. However, the magnitude of which each parameter affects this response is still unclear. The main objective of this work was to determine the magnitude in which soil pH and organic matter fire-induced alterations condition the response of viable and cultivable micro-organisms using laboratory heating, mimicking a range of fire intensities. Four heating treatments were applied to unaltered forest soil: unheated, 300, 450, and 500 °C. In order to isolate the effect of nutrient or pH heating-induced changes, different culture media were prepared using soil:water extracts from the different heated soils, nutrient, and pH amendments. Each medium was inoculated with different dilutions of a microbial suspension from the same original, unaltered soil, and microbial abundance was estimated. Concurrently, freeze-dry aliquots from each soil:water extract were analyzed by pyrolysis-gas chromatography/mass spectrometry. The microbial abundance in media prepared with heated soil was lower than that in media prepared with unheated soil. Nutrient addition and pH compensation appear to promote microbial proliferation in unaltered and low-intensity heated treatments, but not in those heated at the highest temperatures. Soil organic matter characterization showed a reduction in the number of organic compounds in soil-heated treatments and a marked increase in aromatic compounds, which could be related to the observed low microbial proliferation.
X-ray Self-Emission Imaging of Hydrodynamic Laser-Induced Astrophysical Phenomena

In this article, we present an overview of the application of X-ray self-emission methods for the imaging of hydrodynamic astrophysical phenomena in laboratory-scale experiments. Typical diagnostic approaches, their advantages, drawbacks, and application perspectives are considered. We show that X-ray imaging and spectroscopy methods with 2D and even 1D spatial resolution are valuable for numerous laboratory astrophysical problems. Furthermore, the methods revealed the hydrodynamic evolution, the spatial shape and structure, and spatial features of important parameters such as electron density and plasma temperature of astrophysical objects and related phenomena, which are also required for the verification of astrophysical models.
Electronic band structure and photoemission spectra of graphene on silicon substrate

Synergizing graphene on silicon based nanostructures is pivotal in advancing nano-electronic device technology. A combination of molecular dynamics and density functional theory has been used to predict the electronic energy band structure and photo-emission spectrum for graphene-Si system with silicon as a substrate for graphene. The equilibrium geometry of the system after energy minimization is obtained from molecular dynamics simulations. For the stable geometry obtained, density functional theory calculations are employed to determine the energy band structure and dielectric constant of the system. Further the work function of the system which is a direct consequence of photoemission spectrum is calculated from the energy band structure using random phase approximations.
Evaluating and Comparing the Usability of Mobile Banking Applications in Saudi Arabia

In many countries, the rapid growth of the Internet and mobile technologies has led to the expansion of Internet banking, especially mobile banking. Many banks seek to provide integrated banking services through mobile applications (apps) to increase customer satisfaction and loyalty. A quick look at the reviews of the mobile banking apps in Saudi Arabia reveals different usability issues among these apps. This research analyzed, evaluated, and compared the usability of all Saudi mobile banking apps available for the iOS and Android systems. Usability (as defined by ISO 9241) was measured using three criteria—effectiveness, efficiency, and satisfaction. This research also identified and discussed the most critical weaknesses of the Saudi banks’ apps in regard to providing satisfactory solutions to developers. The results showed that the most critical issues existed in the user interfaces and functionality of the apps, especially those that frequently received updates. Furthermore, the lack of customer support made the interaction between banks and customers weak, leading to customer dissatisfaction.
Provably Efﬁcient Causal Model-Based Reinforcement Learning for Environment-Agnostic Generalization

In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly inﬁnite, set of environments. Such environments are modeled as discrete Markov decision processes with both states and actions represented through a feature vector. The underlying structure of the environments allows the transition dynamics to be factored into two components: one that is environment-speciﬁc and another one that is shared. Consider a set of environments that share the laws of motion as an illustrative example. In this setting, the agent can take a ﬁnite amount of reward-free interactions from a subset of these environments. The agent then must be able to approximately solve any planning task deﬁned over any environment in the original set, relying on the above interactions only. Can we design a provably efﬁcient algorithm that achieves this ambitious goal of systematic generalization? In this paper, we give a partially positive answer to this question. First, we provide the ﬁrst tractable formulation of systematic generalization by employing a causal viewpoint. Then, under speciﬁc structural assumptions, we provide a simple learning algorithm that allows us to guarantee any desired planning error up to an unavoidable sub-optimality term, while showcasing a polynomial sample complexity.
Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings

Structure perception is a fundamental aspect of music cognition in humans. Historically, the hierarchical organization of music into structures served as a narrative device for conveying meaning, creating expectancy, and evoking emotions in the listener. Thereby, musical structures play an essential role in music composition, as they shape the musical discourse through which the composer organises his ideas. In this paper, we present a novel music segmentation method, pitchclass2vec, based on symbolic chord annotations, which are embedded into continuous vector representations using both natural language processing techniques and custom-made encodings. Our algorithm is based on long-short term memory (LSTM) neural network and outperforms the state-of-the-art techniques based on symbolic chord annotations in the field.
Understanding Optimization Challenges when Encoding to Geometric Structures

Geometric inductive biases such as spatial curvature, factorizability, or equivariance have been shown to enable learning of latent spaces which better reﬂect the structure of data and perform better on downstream tasks. Training such models, however, can be a challenging task due to the topological constraints imposed by encoding to such structures. In this paper, we theoretically and empirically characterize obstructions to training autoencoders with geometric latent spaces. These include issues such as singularity (e
Prevalence of and Risk Factors for Skin Picking Disorder Symptoms Among Adults in an Arab Middle Eastern Population: A Cross-Sectional Study

study seeks to fill this gap by investigating these factors in a community sample from Jeddah. Methods: This descriptive cross-sectional study was conducted in the city of Jeddah. The Skin Picking Scale-Revised (SPS-R) scale was administered to a convenience sample of 520 respondents. A partial least squares path model (PLS-PM) for “impairment” and “symptoms severity” subscales was assessed by evaluating the validity of measurement and structural models. Results: Skin picking behavior was reported by 28.8% (n=150). A significant level of skin picking disorder symptoms was present in 1.2% (n=6). Skin picking visual effect, depressive symptoms, and being unmarried were the only positive independent predictors of the total SPS-R score. Conclusion: SPD symptoms are relatively common among the adult population in Jeddah, but those with threshold symptoms indicative of SPD are relatively few. Such behavior is particularly common in vulnerable groups such as those with depressive symptoms and the unmarried. More attention to this condition by clinicians will improve the quality of life of those affected, and reduce the emotional and physical health consequences of this often unrecognized condition.
Lipoxygenase (LOX) in Sweet and Hot Pepper (Capsicum annuum L.) Fruits during Ripening and under an Enriched Nitric Oxide (NO) Gas Atmosphere

Lipoxygenases (LOXs) catalyze the insertion of molecular oxygen into polyunsaturated fatty acids (PUFA) such as linoleic and linolenic acids, being the first step in the biosynthesis of a large group of biologically active fatty acid (FA)-derived metabolites collectively named oxylipins. LOXs are involved in multiple functions such as the biosynthesis of jasmonic acid (JA) and volatile molecules related to the aroma and flavor production of plant tissues, among others. Using sweet pepper (Capsicum annuum L.) plants as a model, LOX activity was assayed by non-denaturing polyacrylamide gel electrophoresis (PAGE) and specific in-gel activity staining. Thus, we identified a total of seven LOX isozymes (I to VII) distributed among the main plant organs (roots, stems, leaves, and fruits). Furthermore, we studied the FA profile and the LOX isozyme pattern in pepper fruits including a sweet variety (Melchor) and three autochthonous Spanish varieties that have different pungency levels (Piquillo, Padrón, and Alegría riojana). It was observed that the number of LOX isozymes increased as the capsaicin content increased in the fruits. On the other hand, a total of eight CaLOX genes were identified in sweet pepper fruits, and their expression was differentially regulated during ripening and by the treatment with nitric oxide (NO) gas. Finally, a deeper analysis of the LOX IV isoenzyme activity in the presence of nitrosocysteine (CysNO, a NO donor) suggests a regulatory mechanism via S-nitrosation. In summary, our data indicate that the different LOX isozymes are differentially regulated by the capsaicin content, fruit ripening, and NO.
Vanadium Oxide–Conducting Polymers Composite Cathodes for Aqueous Zinc-Ion Batteries: Interfacial Design and Enhancement of Electrochemical Performance

Aqueous zinc-ion batteries (AZIBs) are being intensively developed as potential alternative electrochemical power sources, due to their advantages such as low cost, high safety, abundance of natural zinc resources and appropriate energy density. Among currently investigated prospective cathode materials for AZIBs, vanadium oxide-based composites with intrinsically conducting polymers have shown many advantages, such as high capacity, high power density and long battery life. This review gives a focused view of the design for the boosting of zinc ion storage performance using intrinsically conducting polymers in vanadium oxide-based composites and the mechanism of intercalation processes. The main challenges in interfacial engineering of vanadium oxide-conducting polymers composite structures and the prospects for further development of such cathode materials are summarized and discussed. The review would give rise to a broad interest focusing on the advantageous strategy of the development of vanadium oxide composite cathodes with intrinsically conducting polymers (polyaniline, polypyrrole, poly(3,4-ethylenedioxythiophene)) for AZIBs with improved energy density, high-rate capability and stability.
Effects of Sowing Date and Nitrogen (N) Application Rate on Grain Yield, Nitrogen Use Efficiency and 2-Acetyl-1-Pyrroline Formation in Fragrant Rice

Purpose: This study aimed to assess the effects of the sowing date and nitrogen application rate on the grain yield, nitrogen use efficiency (NUE), 2-acetyl-△1-pyrroline (2-AP) contents and biochemical parameters related to 2-AP formation in fragrant rice. Methods: A factorial split-plot arrangement of treatments was set up in a split-zone experiment with two factors and three levels carried out for 3 years. The main plots included two sowing dates: April 1st and May 1st. Subplots contained three different nitrogen levels i.e., 0 kg N ha−1 (N0), 120 kg N ha−1 (N1) and 180 kg N ha−1 (N2). Results: The results indicated that compared with April 1st, the delay in the sowing date (May 1st) decreased the number of grains per panicle, 1000-grain weight, grain yield, NUE and contents of 2-AP, △1-pyrroline, proline and pyrroline-5-carboxylic acid (P5C), as well as the proline dehydrogenase (ProDH) activity. Furthermore, compared with N0, higher-N (N1 and N2) applications increased the panicle number, the number of grains per panicle, grain yield and contents of 2-AP, △1-pyrroline, proline and P5C, as well as the activities of ProDH and ornithine aminotransferase (OAT). The seed-setting rate, 1000-grain weight and NUE were decreased under N2 compared to N1, but the 2-AP content and yield were higher under the high-N application N2 (180 kg ha−1) compared to N1 (120 kg ha−1). Conclusions: Sowing on April 1st and the N2 (180 kg ha−1) application improved the yield and promoted 2-AP biosynthesis, while sowing on May 1st reduced the yield and 2-AP content. Therefore, sowing on April 1st with 180 kg ha−1 of nitrogen is the best, which can not only ensure the yield but also improve the fragrant quality of rice.
News Exposure and Psychological Adjustment: Examining the Emotional Effects and Correlates of Short and Long-Term Exposure to Soft and Hard News

Past research has shown that news media may contain a disproportionate amount of negative news. Frequent exposure to such negative information could have detrimental effects on our mental well-being. We aimed to gain further insight into the potentially adverse effects of exposure to soft and hard news, as well as to examine potential reasons why individuals might expose themselves to such negative information. To do so, we conducted an online survey involving 176 participants (66 male, 107 female and 3 other) aged 15–65 years. The study included manipulation and additional (correlational) analyses. In the manipulation, we tested for the potential short-term effects of exposure to soft or hard news on the psychological well-being of our participants (as measured by the semi-projective Rotter Incomplete Sentences Blank; Rotter, 1950). This was done by setting three conditions (soft news, hard news and control group) wherein participants were exposed to 15 consecutive front page screenshots of the chosen soft and hard newspapers respectively. Hard news is generally more focused on major issues and breaking events – i.e., news that is important for the individual to understand, while soft news usually focuses on personal stories, is less time-bound, and is more incident-based. The correlational part of our study focused on discovering associations between long-term exposure to (different types of) news and the degree of negative emotions and well-being (measured by the DASS-21, Lovibond & Lovibond, 1995) and Rosenberg Self Esteem Scale (Rosenberg, 1965). The results did not show statistically significant differences between conditions. When comparing the long-term readers of the chosen hard and soft newspapers, statistically significant differences were found only in anxiety levels, however, a forming trend seemed to suggest that long-term exposure to soft news might be associated with reduced psychological well-being. Our findings are discussed in line with the contemporary psychological literature.
Glycerol Hydrogenolysis to 1,2-Propanediol over Novel Cu/ZrO 2 Catalysts

: Glycerol is the main by-product of biodiesel production; its upgrading to more valuable products is a demanding issue. Hydrogenolysis to 1,2-propanediol is one of the most interesting processes among the possible upgrading routes. In this study, we propose novel copper/zirconia catalysts prepared by advanced preparation methods, including copper deposition via metal–organic framework (MOF) and support preparation via the sol–gel route. The catalysts were characterized by N 2 physisorption, X-ray diffraction, Scanning Electron Microscopy, H 2 -TPR and NH 3 -TPD analyses and tested in a commercial batch reactor. The catalyst prepared by copper deposition via MOF decomposition onto commercial zirconia showed the best catalytic performance, reaching 75% yield. The improved catalytic performance was assigned to a proper combination of redox and acid properties. In particular, a non-negligible fraction of cuprous oxide and of weak acid sites seems fundamental to preferentially activate the selective pathway. In particular, these features avoid the overhydrogenolysis of 1,2-propanediol to 1-propanol and enhance glycerol dehydration to hydroxyacetone and the successive hydrogenation of hydroxyacetone to 1,2-propanediol.
Target-based Surrogates for Stochastic Optimization

We consider minimizing functions for which it is expensive to compute the (possibly stochastic) gradient. Such functions are prevalent in reinforcement learning, imitation learning and adversarial training. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a \emph{target space} (e.g. the logits output by a linear model for classification) that can be minimized efficiently. This allows for multiple parameter updates to the model, amortizing the cost of gradient computation. In the full-batch setting, we prove that our surrogate is a global upper-bound on the loss, and can be (locally) minimized using a black-box optimization algorithm. We prove that the resulting majorization-minimization algorithm ensures convergence to a stationary point of the loss. Next, we instantiate our framework in the stochastic setting and propose the $SSO$ algorithm, which can be viewed as projected stochastic gradient descent in the target space. This connection enables us to prove theoretical guarantees for $SSO$ when minimizing convex functions. Our framework allows the use of standard stochastic optimization algorithms to construct surrogates which can be minimized by any deterministic optimization method. To evaluate our framework, we consider a suite of supervised learning and imitation learning problems. Our experiments indicate the benefits of target optimization and the effectiveness of $SSO$.
Determination of the Dolines Phenomenon Using by Geological and Geoelectrical Resistivity Survey Approach in Bedoyo Village, Ponjong Subdistrict

Gunung Kidul Regency is a highland consisting of carbonate rocks dominantly. Bedoyo village in Ponjong sub district has a lot of dolines, but in the summer season, the dolines become dry and could not be water storage. Geological mapping is included in geomorphological, rock observation, structural mapping also petrographical analysis in the laboratory. A Geophysics survey is to observe the subsurface rock distribution laterally and vertically with dipole-dipole configuration. The resistivity method is to determine the resistivity value for the depth of fewer than 40 meters with the length of electrodes of about 250 meters. The result of the geological and geophysical analysis is to review the dolines phenomenon that could not be a shallow aquifer. It is caused by the subsurface condition beneath the dolines consisting of carbonated rocks with poor porosity, lenses of impermeable tuffaceous claystone, and igneous rock. Those rock basements have a moderate to poor porosity in the aquifer. The less opportunity to be a good aquifer if the deeper rock is an impermeable igneous rock. An alternative geophysical survey is needed to observe the deeper rock vertically.
Cytomegalovirus Colitis in Recent COVID-19 Infection: A Case Report

Human cytomegalovirus (CMV) is a double-stranded DNA virus with high seroprevalence and is common in developing countries. The ability to cause a lifelong latent infection is established, and it can be reactivated later in life in both immunocompetent and especially in immunosuppressed carriers. We report a case of CMV colitis in a gentleman who was initially admitted for COVID-19 pneumonia