Miracle-dz/UniFAR-FTUs huggingface dataset:
A Matrix Completion Solution to the Problem of Ignoring the Ignorability Assumption

Digital platforms deliver numerous interventions to their users. One of their main goals is to estimate the causal effect of these interventions. An ideal way to answer this question is to run a fully randomized experiment. However, the economic cost of such experiments is high, making alternative approaches based on observational data appealing to digital platforms. In this paper, we study the feasibility of using observational methods in the presence of algorithmic decision-making. A central assumption needed for observational studies is the strong ignorability assumption, which requires the unconfoundedness of the treatment assignment, and an often-ignored part: overlap assumption that requires the assignment to be non-deterministic. Although the setting created by algorithmic decision-making satisfies the unconfoundedness assumption as the assignment rule is known, the overlap assumption is often violated because these algorithms generate deterministic recommendations. We theoretically show that the violation of overlap can substantially bias the estimates of the average treatment effect from observational data. We quantify this bias and discuss whether it is practically relevant in digital platforms. To address this issue, we propose a novel solution based on machine learning methods used for matrix completion that allows us to recover the average treatment effect estimates if the underlying space of treatment effects is low-rank. We use our algorithm to develop statistical tests to examine whether the lack of overlap results in substantial bias in the main estimates of the causal parameters. Finally, we validate our theoretical results using synthetic data and discuss the implications.
Cloud Forensics: Exploring the Challenges and Mapping Out Solutions for the Future.

With its multiple transformational effects on computing operations and myriad commercial and technological potential, cloud computing is a fast developing technology. However, appropriate forensic capabilities that could aid in the investigation of illegal activity in the cloud have not yet been established by cloud service providers and clients. The difficulty of seizing a suspect’s computer and accessing their files without breaching the privacy of many other users is a result of the fact that storage systems are no longer local and that each cloud server now houses files from numerous users. Since a lack of digital investigation may increase hazards against cloud settings, cloud forensic investigations must be acknowledged for any occurrence that occurs in cloud services. The steps of identification, collection, inspection, analysis, and reporting are the focus of this study's evaluation of how to conduct forensic investigations on cloud computing in light of these challenges. Due to challenges with jurisdiction, data duplication, and multi-tenancy on the cloud platform, locating, identifying, and isolating the suspected or compromised targets is challenging. In contrast to earlier studies, this study distinguishes between the issues and potential solutions for non-cloud and cloud digital forensics by basing its analysis on the phases of traditional digital forensics. The study discusses the difficulties in cloud forensics and their remedies, and the presentation aids investigators in better understanding the issues in cloud systems.
The Representation of Women in Online Advertisements: Challenging or Perpetuating Gender Stereotypes?

Nowadays almost everyone uses the Internet to search for various types of information. As a result, various websites offering a multitude of services and products are found online. Numerous websites are found to provide information online because all types of information can easily be found online. This paper is a study on online advertisements in Malaysia and the way gender is represented. This study has found that there is stereotypical representation of women as being less successful, subservient and in other less flattering terms. It is recommended that women be portrayed in a positive light and should be acknowledged for their respective contribution to the community. This in turn, will create a favorable impression of women and earn the respect of the community which will tear down the traditional misconception about the role of women in the society. This study is important in highlighting the differences of gender representation found in advertisements and the effects on the perception of the readers. Violence and discrimination against women can also be prevented with the presence of gender equality in advertising. Moreover, a positive, equal, and respectful relationship can be fostered while stereotyped representation of gender roles can be prevented.
DETECTION OF CYBERBULLYING ON SOCIAL MEDIA USING XG-BOOST ALGORITHM

-Cyberbullying is a extreme on-line problem that influences younger human beings and adults. Hence accidents, inclusive of dying and disappointment. Content moderation on social media systems is becoming a growing necessity. The following study makes use of information from exclusive styles of cyberbullying, Twitter hate tweets and remarks based totally on non-public assaults from Wikipedia boards, to build a version on how cyberbullying is detected in text information using herbal language processing and machine gaining knowledge of. Three characteristic extraction strategies and 4 classifiers had been studied to determine the best technique. For these tweets, the version is greater than ninety% correct, and for the Wikipedia facts it's miles extra than 80%.
Winter sensitivity of glacial states to orbits and ice sheet heights in CESM1.2

The changing climate system between icehouse and greenhouse states during the Quaternary Period were dramatic, yet the magnitude of these changes are still uncertain due to unconstrained ice sheets and a lack of transition mechanisms. In this study, we investigate the individual and combined impact of ice sheet heights, orbital configurations, and greenhouse gas changes for a range of Quaternary climate states to assess the linearity or non-linearity of the climate system’s response. To this end, we conduct two sets of sensitivity experiments: a series on the Preindustrial (PI) climate and experiments on 5 Quaternary glacial states. First, modifying the PI conditions with respect to orbit, Greenland ice sheet height, and greenhouse gasses, we find that the climate system’s response to the individual factors do not superimpose on the combined response of the climate system. Thus, already under PI conditions the climate system responds in a non-linear fashion. Second, our results on Quaternary glacial states show that changing ice sheet height is the primary cause of changes in climate systems, regardless of orbit. But the subtle regional effects that orbit has are not always explained by ice sheet height changes. As for 10 the PI simulations we find a strong, non-linear behavior in combined orbital-ice sheet height effect, where the response of the atmospheric circulation plays an important role. Therefore, orbit, ice sheets, and greenhouse gasses evolve through time by specific pathways and imply a theoretical constraint on the real climate state. As the spatial and temporal resolution of the Quaternary proxy data improves, combined with these modeled climates, we expect to generate substantial constraints on the number of realistic Quaternary climate states.
RELATIONSHIP BETWEEN PROCRASTINATION AND INTERNET ADDICTION AMONG PHYSIOTHERAPY STUDENTS

INTRODUCTION – The internet has become worldwide medium for research, information exchange, entertainment, business and communication . Overutilization of internet has led to a negative quality of life in terms of health . Procrastination is a propensity which is seen in many individuals to the extent specialist ’ s trust that it is one of the characteristic inclinations of the human . Many people find themselves on the internet as surfing while they should be working or studying on a specific task . These people put off their work or academic related responsibilities and spend excessive amount of time on the internet . This kind of behavior was regarded as modern type of procrastination mediated by the internet . MATERIALS AND METHODOLOGY : For this study,180 participants were approached . The study was conducted using PQ scale and IAT - 20 in the physiotherapy students of different colleges and universities and 150 responses were collected . Spearman ’ s correlation test was performed to find the correlation between Procrastination and Internet Addiction . RESULT – Result showed that there is positive correlation between PQ and IAT - 20 . CONCLUSION – There is moderate to high correlation between procrastination and internet addiction
ZK-for-Z2K: MPC-in-the-Head Zero-Knowledge Proofs for Z 2 k

In this work, we extend the MPC-in-the-Head framework, used in recent efficient zero-knowledge protocols, to work over the ring Z 2 k , which is the primary operating domain for modern CPUs. The proposed schemes are compatible with any threshold linear secret sharing scheme and draw inspiration from MPC protocols adapted for ring operations. Additionally, we explore various batching methodologies, leveraging Shamir’s secret sharing schemes and Galois ring extensions
Pituitary Adenoma: A Prospective Study Prediction of Cavernous Sinus Invasion by Based Reconstruction for Preoperative - Thin-Slice Pituitary MRI with Deep Learning

BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning – based reconstruction can better predict cavernous sinus invasion by pituitary adenoma preoperatively and to estimate the depth of invasion and degree of contact in relation to the carotid artery, compared with 3-mm-slice-thickness MRI. MATERIALS AND METHODS: This single-institution, prospective study included 67 consecutive patients (mean age, 53 [SD, 12] years; 28 women), between January and August 2020, who underwent a combined contrast-enhanced T1-weighted imaging protocol of 1-mm-slice-thickness MRI 1 deep learning – based reconstruction and 3-mm-slice-thickness MRI. An expert neuroradiologist who was blinded to the imaging protocol determined cavernous sinus invasion using the modi ﬁ ed Knosp classi ﬁ cation on 1-mm-slice-thickness MRI 1 deep learning – based reconstruction and 3-mm-slice-thickness MRI, respectively. Reference standards were established by the consensus of radiologic, intraoperative, pathologic, and laboratory ﬁ ndings. The primary end point was the diagnostic performance of each imaging protocol, and the secondary end points included depth of invasion and degree of contact in relation to the carotid artery. RESULTS: The diagnostic performance of 1-mm-slice-thickness MRI 1 deep learning – based reconstruction (area under the curve, 0.79; 95% CI, 0.69 (cid:1) 0.89) in predicting cavernous sinus invasion by pituitary adenoma was higher than that of 3-mm-slice-thickness MRI (area under the curve, 0.61; 95% CI, 0.52 – 0.70; P , .001). One-millimeter-slice-thickness MRI 1 deep learning – based reconstruction demonstrated greater depth of invasion by pituitary adenomas from the medial intercarotid line than 3-mm-slice-thickness MRI (4.07 versus 3.12 mm, P , .001). A higher proportion of cases were in a greater degree of contact with the intracavernous ICA with 1-mm-slice-thickness MRI 1 deep learning – based reconstruction than with 3-mm-slice-thickness MRI (total encasement, 37.3% versus 13.4%, P , .001; . 270°, 38.8% versus 16.4%, P , .001). CONCLUSIONS
Unveiling the importance of diffusion on the deterioration of cellulose acetate artefacts: The profile of plasticiser loss as assessed by infrared microscopy

Cellulose acetate (CA) artefacts are one of the most valued plastic items in museum collections and are known to present stability issues, with the loss of plasticiser being among the main degradation processes. This study investigates the concentration distribution of diethyl phthalate (DEP) plasticiser throughout the dimensions of CA using infrared microscopy for the first time. Artificial ageing experiments us-ing reference and historic CA plasticised with DEP were performed to assess the change in the concentration profiles as a function of ageing
Executive Compensation and Value of Listed Deposit Money Banks in Nigeria

The increasing failure of banks has made it important to seek for ways to enhance its value in order to attract investors and potential investors. To make this reality, scholars have argued from various quarters that the people who manage the banks must be adequately compensated if the desired value needs to be achieved. Therefore, the study examines the relationship between executive compensation and value of listed deposit money banks (DMB) in Nigeria. The study adopted correlational research design with balanced panel data of 14 listed banks which served as population of the study for the period of 2010-2021 using Generalized Least Square (GLS) regression as a tool of analysis. The study found that CEO Pay and Chairman’s compensation have positive effect on the value of listed banks, while the highest paid director exact negative influence on the banks’ value. This implies that the CEO Pay and Chairman’s compensation improves the value of banks. Therefore, it is recommended among others that the management of banks should increase the CEO pay and place more emphasis on performance as a basis of increased pay to guarantee continuous improvement in the value of the banks.
Supplementary Material for CVPR 2023 Paper: Object Detection with Self-Supervised Scene Adaptation

In this supplementary document, we show more sample frames and distribution of camera locations of Scenes100 to illustrate its diversity. We provide more implementation details and the running speed for the experiments in the main paper. We also look into the quality of the pseudo-labeling and model performance from the perspective of individual videos. Lastly we show the re-sults of compound adaptation experiments. Please refer to https://github.com/cvlab-stonybrook/scenes100 for the dataset and code. It is recommended to read this document on a color screen, and zoom in for fine details in the figures.
Internet Appendix for “Overconfidence and Preferences for Competition”

Section I presents a detailed analysis of whether MBAs who consented to the study in 2006 vary systematically from MBAs who did not. Section II presents a similar analysis of whether MBAs who answered the survey in 2015 vary systematically from those who did not. Section III describes in detail the numerous robustness checks reported in the paper but not fully described there due to space constraints. Section IV describes the procedures used to conduct the experiment and survey, including a sample of the instructions used to elicit preferences for competition. Lastly, Section V describes the additional variables used in the robustness checks.
Estimation of Hardness of Indentation Made with a Conical Indenter Using Numerical Slip-Line Field Technique

When a rigid wedge is indented in to a semi-infinite block, the material is bulged up around the wedge that is generally called lip. The previous works in this filed considered the outer profile of the lip to be linear. But, present authors observed both experimentally and with the aid of finite element analysis that the profile of the lip is not always linear, and it depends on the angle of the wedge and friction parameters. So, in this work, attempts have been made to calculate hardness of indentation for different wedge angles and friction parameters. As hardness is intrinsic property of material, consideration of either linear or parabolic lip will not be affected much. A comparative study of hardness for linear and parabolic free surface profiles of the piled up material around the cone is analyzed in this work.
Supplementary Material for “Holistic Interaction Transformer Network for Action Detection”

As stated in the paper, we use a memory span S = 30 and the total number of cached frame features passed to the TI module is 2S + 1 (features of the current frame and 30 frames from each side of the current frame). Inspired by [4] and [5], we keep a cached memory pool M with temporal person features, and at the end of each iteration, we update the cached person features. This dynamic approach does not increase the computational cost as the number of frame increases. It also allows us to train the model end-to-end since we don’t need to pre-train and freeze memory features as is the case with [2] and [1].
Dynamic emotion recognition using histogram of oriented gradients

: A dynamic emotion recognition model is being developed for the use in a closed loop feedback system to support the emotional development of children with autism spectrum disorder. A neural network model is designed to learn the patterns in the acquired data. The model takes in input the sequence of features extracted from the traditional feature extraction technique of histogram of oriented gradients. The model performance is assessed by the classification accuracies of the OULU-CASIA database validation set. Accuracies of up to 43.64% were achieved. This preliminary model marks the first step in developing a robust dynamic emotion recognition platform.
Learnable Skeleton-Aware 3D Point Cloud Sampling

Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-to-sample methods have recently received increasing attention from the community for jointly training with downstream tasks. However, the abovementioned task-specific sampling methods usually fail to explore the geometries of objects in an explicit manner. In this paper, we introduce a new skeleton-aware learning-to-sample method by learning object skeletons as the prior knowledge to preserve the object geometry and topology information during sampling. Specifically, without labor-intensive annotations per object category, we first learn category-agnostic object skeletons via the medial axis transform definition in an unsupervised manner. With object skeleton, we then evaluate the histogram of the local feature size as the prior knowledge to formulate skeleton-aware sampling from a probabilistic perspective. Additionally, the proposed skeleton-aware sampling pipeline with the task network is thus end-to-end trainable by exploring the reparameterization trick. Extensive experiments on three popular downstream tasks, point cloud classification, retrieval, and reconstruction, demonstrate the effectiveness of the proposed method for efficient point cloud analysis.
Study on the Influential Factors of Noise Characteristics in Dense-Graded Asphalt Mixtures and Field Asphalt Pavements

Determining the influential factors of noise characteristics in dense-graded asphalt mixtures and field asphalt pavement is important in constructing highways that are both low noise and environmentally friendly. In this study, the effects of nominal maximum aggregate size, asphalt binder type, air void percentage, and the service life of pavement on the noise absorption characteristics of asphalt mixtures were first investigated in laboratory. Thereafter, tire/pavement noise measurements were conducted on different types of dense-graded asphalt pavements. The effects of the service lives of the pavements, the types of the pavements, driving speeds, and test temperatures on the noise levels of the pavements were also studied. The Zwicker method is used to calculate psychoacoustic parameters on the tire/pavement noise spectrum. The laboratory results indicate that reducing the nominal maximum aggregate size, using rubber asphalt, and increasing air void percentage as well as surface texture depth improve the sound absorption performance of asphalt mixtures. The field measurements show that laying down asphalt pavements with a shorter service life or larger texture depth, using rubber asphalt, reducing traffic speed, and increasing air temperature can reduce noise.
Towards an improved understanding of wildfire CO emissions: a satellite remote-sensing perspective

Emissions from wildfires are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometers downwind. These emissions can be estimated by a bottom-up approach, using inputs such fuel type, burned area, and standardized emission factors. Emissions are also commonly derived with a top-down approach, us-ing satellite observed fire radiative power (FRP) as proxy for fuel consumption. More recently, wildfire emissions have been demonstrated to be estimated directly from satellite observations, including carbon monoxide (CO). Here, we explore the po-5 tential of satellite-derived CO emission rates from wildfires and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tro-pospheric Monitoring Instrument) high spatial-resolution satellite datasets to create a global inventory database of burning emissions CO emissions between 2019 and 2021. Our retrieval methodology includes an analysis of conditions under which emission estimates may be inaccurate and filters these accordingly. Additionally, we determine biome specific emission coef-10 ficients (emissions relative to FRP) and show how combining the satellite derived CO emissions with satellite observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS) establishes an annual CO emission budget from wildfires. The resulting emissions totals are compared to other top-down and bottom-up emission inventories over the past two decades. In general, the satellite-derived emissions inventory values and bottom-up emissions inventories have similar CO
Intelligence Challenges in Contemporary Geopolitical Discourse By

IntroductionIntelligence is critical in the state's decision-making process and national security strategy. Nevertheless, the contemporary regional and geopolitical complexity implies countless challenges to intelligence. Carl von Clausewitz said: "In the fast-moving complex environment, the target has changed by the time you adopt a plan. " (Clausewitz, 1832) Today this sentence has more relevance. We live in an interregnum period where the domination of liberal democracy is challenged from the inside out through the war in Ukraine, a decline of trust in democracy, inequality, division of societies, decay of economic development, rising inflation, and geopolitical competition between great powers. Today we live in a hazy space where there is no clear line between war and peace. Further, we started our twenty-first century with a dangerous relationship between political leaders and their intelligence advisors, in which they are distorting intelligence information to justify their political decisions.