Miracle-dz/UniFAR-FTUs huggingface dataset:
Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data

How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classification algorithms tasked with predicting outcomes accurately from these datasets tend to replicate these biases. We advocate a causal modeling approach to learning from biased data, exploring the relationship between fair classification and intervention. We propose a causal model in which the sensitive attribute confounds both the treatment and the outcome. Building on prior work in deep learning and generative modeling, we describe how to learn the parameters of this causal model from observational data alone, even in the presence of unobserved confounders. We show experimentally that fairness-aware causal modeling provides better estimates of the causal effects between the sensitive attribute, the treatment, and the outcome. We further present evidence that estimating these causal effects can help learn policies that are both more accurate and fair, when presented with a historically biased dataset.
Commodity Recommendation for Users Based on E-commerce Data

With the popularity of mobile devices and the development of e-commerce, more and more people choose to buy items in the mobile terminal. Therefore the mobile terminal commodity recommendation services and commodity recommendation algorithms are more and more important. Aim at this problem, this paper conducts a study of predicting the user's purchase behavior based on the online distribution platform and the desensitization data sets provided by the Chinese largest electricity platform Alibaba. Based on the GBDT (Gradient Boosting Decision Tree) model, by using ODPS (Open Data Processing Service) and Python to simultaneously implement machine learning and training online and offline respectively, and combining with the user behavior sequence recorded over a period of time, the user purchase behavior at a later time will be properly predicted.
A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media

Powered by machine learning techniques, social media provides an unobtrusive lens into individual behaviors, emotions, and psychological states. Recent research has successfully employed social media data to predict mental health states of individuals, ranging from the presence and severity of mental disorders like depression to the risk of suicide. These algorithmic inferences hold great potential in supporting early detection and treatment of mental disorders and in the design of interventions. At the same time, the outcomes of this research can pose great risks to individuals, such as issues of incorrect, opaque algorithmic predictions, involvement of bad or unaccountable actors, and potential biases from intentional or inadvertent misuse of insights. Amplifying these tensions, there are also divergent and sometimes inconsistent methodological gaps and under-explored ethics and privacy dimensions. This paper presents a taxonomy of these concerns and ethical challenges, drawing from existing literature, and poses questions to be resolved as this research gains traction. We identify three areas of tension: ethics committees and the gap of social media research; questions of validity, data, and machine learning; and implications of this research for key stakeholders. We conclude with calls to action to begin resolving these interdisciplinary dilemmas.
Classification Algorithmof Chinese SentimentOrientation Based on Dictionary and LSTM

Chinese sentiment analysis is a hot research issue in information analysis, but the tagging corpus which can be used for machine learning algorithm training is poor. Machine learning algorithm is used for text sentiment classification, generally only categories are given while sentiment words can not be extracted. This paper proposed an automatic tagging strategy for training corpus and a classification algorithm for Chinese sentiment orientation based on dictionary and LSTM. It can label the training corpus automatically and accurately and efficiently, and also extract sentiment words. Experiment shows this method is effective and the accuracy of LSTM algorithm has reached 93.51% on the mixed data set of sentiment classification.
Tissue Region Growing for Hispathology Image Segmentation

The accurate identification of the tumour tissue border is of crucial importance for histopathology image analysis. However, due to the high morphology variance in histology images, especially in border regions where cancer tissue interfere into the normal region, it is challenging even for the pathologists to define the border, not to say for the machine. In this paper, we present an innovative framework to semantically segment the tumour border area in colorectal liver metastasis (CRLM) on pixel level by integrating the features from deep convolutional networks with spatial and statistical information of the cells. With annotations from the pathologists, a two-level deep neural network including a cell-level model and a tissue-level model, is trained to classify patches from the whole slide scan image. Based on the prediction of trained models, a growing-style algorithm is proposed to finalize the segmentation by leveraging the statistical and spatial properties of the cells. Evaluated against the ground truth created by the experts, the framework demonstrates a significant improvement over a conventional deep network model on the cell-level model or the tissue model alone.
Harvesting Row-Buffer Hits via Orchestrated Last-Level Cache and DRAM Scheduling for Heterogeneous Multicore Systems

In heterogeneous multicore systems, the memory subsystem, including the last-level cache and DRAM, is widely shared among the CPU, the GPU, and the real-time cores. Due to their distinct memory traffic patterns, heterogeneous cores result in more frequent cache misses at the last-level cache. As cache misses travel through the memory subsystem, two schedulers are involved for the last-level cache and DRAM, respectively. Prior studies treated the scheduling of the last-level cache and DRAM as independent stages. However, with no orchestration and limited visibility of memory traffic, neither scheduling stage is able to ensure optimal scheduling decisions for memory efficiency. Unnecessary precharges and row activations happen in DRAM when the memory scheduler is ignorant of incoming cache misses, and DRAM row-buffer states are invisible to the last-level cache. In this article, we propose a unified memory controller for the the last-level cache and DRAM with orchestrated schedulers. The memory scheduler harvests row-buffer hit opportunities in cache request buffers during spare time without inducing significant implementation cost. We further introduce a dynamic orchestrated scheduling policy to improve memory efficiency while achieving target CPU IPC. Extensive evaluations show that the proposed controller improves the total memory bandwidth of DRAM by 16.8% on average and saves DRAM energy by up to 29.7% while achieving comparable CPU IPCs. With the dynamic scheduling policy, the unified controller achieves the same IPC as the conventional design and increases DRAM bandwidth by 9.2%. In addition, we explore the potential of the proposed memory controller to attain improvements on both memory bandwidth and CPU IPC.
FoxIT: enhancing mobile users' privacy behavior by increasing knowledge and awareness

Albeit offering many benefits, smartphones can pose a severe privacy threat to users. While some users might simply be not aware of privacy issues, others are highly motivated to protect their data, but lack the ability and knowledge to do so. We developed an Android-based application called "FoxIT", which provides users with several education modules as well as a static smartphone and app permission analysis to increase both, privacy awareness and knowledge of mobile users. We conducted a first evaluation of FoxIT in a two-week field study with 31 users and were able to show that use of FoxIT not only leads to increased privacy awareness, but also improves knowledge about privacy related topics. Participants also reported to have improved the privacy conditions on their smartphone, actively informed themselves about privacy related topics, and prompted others to protect their data after using FoxIT. Our results indicate that it might be a promising approach to improve mobile users' privacy behavior by raising awareness and providing background information about privacy related topics.
Development of an Electromagnetic Tracking System for Surgical Instruments Positioning

Electromagnetic tracking systems are widely used in image-guided surgery to detect the position of surgical instrument in the patient's anatomical 3D model. The present study proposes the development and evaluation of a new EMTS able to provide a wide tracking volume. The innovation of this EMTS mainly consists in the design of a suitable Field Generator (FG), including five coils properly arranged in order to increase the magnetic field and, as a consequence, the sensor sensitivity in the working volume. Different methods to convert sensor voltage values in the Cartesian coordinates were investigated and compared in term of position accuracy. The results of experimental tests show good performances of the developed system that allows to increase the distance between sensor and FG with respect to currently EMTS on markets, ensuring a high accuracy of the measurement.
Understanding Event Organization at Scale in Event-Based Social Networks

Understanding real-world event participation behavior has been a subject of active research and can offer valuable insights for event-related recommendation and advertisement. The emergence of event-based social networks (EBSNs), which attracts online users to host/attend offline events, has enabled exciting new research in this domain. However, most existing works focus on understanding or predicting individual users’ event participation behavior or recommending events to individual users. Few studies have addressed the problem of event popularity from the event organizer’s point of view. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in five major cities around the world. We analyze and model four contextual factors: spatial factor using location convenience, quality, popularity density, and competitiveness; group factor using group member entropy and loyalty; temporal factor using temporal preference and weekly event patterns; and semantic factor using readability, sentiment, part of speech, and text novelty. In addition, we have developed a group-based social influence propagation network to model group-specific influences on events. By combining the COntextual features and Social Influence NEtwork, our integrated prediction framework COSINE can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Detailed evaluations demonstrate that our COSINE framework achieves high accuracy for event popularity prediction in all five cities with diverse cultures and user event behaviors.
Real Time Monitoring of Social Media and Digital Press

Talaia is a platform for monitoring social media and digital press. A configurable crawler gathers content with respect to user defined domains or topics. Crawled data is processed by means of the EliXa Sentiment Analysis system. A Django powered interface provides data visualization for a user-based analysis of the data. This paper presents the architecture of the system and describes in detail its different components. To prove the validity of the approach, two real use cases are accounted for: one in the cultural domain and one in the political domain. Evaluation for the sentiment analysis task in both scenarios is also provided, showing the capacity for domain adaptation.
Agent-Based Modelling Approach for Distributed Decision Support in an IoT Network

An increasing number of emerging applications, e.g., internet of things, vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system, as an example of an Internet of Things (IoT) network. We model, analyze and compare various Medium Access Control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high level MAC protocol by using three different approaches: Centralized Decision Maker, DESYNC and decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple decision makers in order to improve the accuracy of information and spectrum utilization of the system.
Early Salient Region Selection Does Not Drive Rapid Visual Categorization

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most relevant candidate portions of an image. Though this strategy has indeed led to improved processing time efficiency in machine algorithms, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.
Balancing Cost and Dissatisfaction in Online EV Charging under Real-time Pricing

We consider an increasingly popular demand-response scenario where a user schedules the flexible electric vehicle (EV) charging load in response to real-time electricity prices. The objective is to minimize the total charging cost with user dissatisfaction taken into account. We focus on the online setting where neither accurate prediction nor distribution of future real-time prices is available to the user when making irrevocable charging decision in each time slot. The emphasis on considering user dissatisfaction and achieving optimal competitive ratio differentiates our work from existing ones and makes our study uniquely challenging. Our key contribution is two simple online algorithms with the best possible competitive ratio among all deterministic algorithms. The optimal competitive ratio is upper-bounded by $\displaystyle \min\{\sqrt{\alpha/p_{\min}},\ p_{\max}/p_{\min}\}$ and the bound is asymptotically tight with respect to $\alpha$, where $p_{\max}$ and $p_{\min}$ are the upper and lower bounds of real-time prices and $\alpha \geq p_{\min}$ captures the consideration of user dissatisfaction. The bounds under small and large values of $\alpha$ suggest the fundamental difference of the problems with and without considering user dissatisfaction. Simulation results based on real-world traces corroborate our theoretical findings and show that the empirical performance of our algorithms can be substantially better than its theoretical worst-case guarantee. Moreover, our algorithms achieve large performance gains as compared to conceivable alternatives. The results also suggest that increasing EV charging rate limit decreases overall cost almost linearly.
Model and Algorithm for Time-Consistent Risk-Aware Markov Games

In this paper, we propose a model for non-cooperative Markov games with time-consistent risk-aware players. In particular, our model characterizes the risk arising from both the stochastic state transitions and the randomized strategies of the other players. We give an appropriate equilibrium concept for our risk-aware Markov game model and we demonstrate the existence of such equilibria in stationary strategies. We then propose and analyze a simulation-based $Q$-learning type algorithm for equilibrium computation, and work through the details for some specific risk measures. Our numerical experiments on a two player queuing game demonstrate the worth and applicability of our model and corresponding $Q$-learning algorithm.
The Theory of Traces for Systems with Nondeterminism and Probability

This paper studies trace-based equivalences for systems combining nondeterministic and probabilistic choices. We show how trace semantics for such processes can be recovered by instantiating a coalgebraic construction known as the generalised powerset construction. We characterise and compare the resulting semantics to known definitions of trace equivalences appearing in the literature. Most of our results are based on the exciting interplay between monads and their presentations via algebraic theories.
Systimator: A Design Space Exploration Methodology for Systolic Array based CNNs Acceleration on the FPGA-based Edge Nodes

The evolution of IoT based smart applications demand porting of artificial intelligence algorithms to the edge computing devices. CNNs form a large part of these AI algorithms. Systolic array based CNN acceleration is being widely advocated due its ability to allow scalable architectures. However, CNNs are inherently memory and compute intensive algorithms, and hence pose significant challenges to be implemented on the resource-constrained edge computing devices. Memory-constrained low-cost FPGA based devices form a substantial fraction of these edge computing devices. Thus, when porting to such edge-computing devices, the designer is left unguided as to how to select a suitable systolic array configuration that could fit in the available hardware resources. In this paper we propose Systimator, a design space exploration based methodology that provides a set of design points that can be mapped within the memory bounds of the target FPGA device. The methodology is based upon an analytical model that is formulated to estimate the required resources for systolic arrays, assuming multiple data reuse patterns. The methodology further provides the performance estimates for each of the candidate design points. We show that Systimator provides an in-depth analysis of resource-requirement of systolic array based CNNs. We provide our resource estimation results for porting of convolutional layers of TINY YOLO, a CNN based object detector, on a Xilinx ARTIX 7 FPGA.
Sharing emotions at scale: The Vent dataset

The continuous and increasing use of social media has enabled the expression of human thoughts, opinions, and everyday actions publicly at an unprecedented scale. We present the Vent dataset, the largest annotated dataset of text, emotions, and social connections to date. It comprises more than 33 millions of posts by nearly a million users together with their social connections. Each post has an associated emotion. There are 705 different emotions, organized in 63 “emotion categories”, forming a two-level taxonomy of affects. Our initial statistical analysis describes the global patterns of activity in the Vent platform, revealing large heterogeneities and certainly remarkable regularities regarding the use of the different emotions. We focus on the aggregated use of emotions, the temporal activity, and the social network of users, and outline possible methods to infer emotion networks based on the user activity. We also analyze the text and describe the affective landscape of Vent, finding agreements with existing (small scale) annotated corpus in terms of emotion categories and positive/negative valences. Finally, we discuss possible research questions that can be addressed from this unique dataset.
Deep Learning-Aided Trainable Projected Gradient Decoding for LDPC Codes

We present a novel optimization-based decoding algorithm for LDPC codes that is suitable for hardware architectures specialized to feed-forward neural networks. The algorithm is based on the projected gradient descent algorithm with a penalty function for solving a non-convex minimization problem. The proposed algorithm has several internal parameters such as step size parameters, a softness parameter, and the penalty coefficients. We use a standard tool set of deep learning, i.e., back propagation and stochastic gradient descent type algorithms, to optimize these parameters. Several numerical experiments show that the proposed algorithm outperforms the belief propagation decoding in some cases.
Integrating and querying similar tables from PDF documents using deep learning

Large amount of public data produced by enterprises are in semi-structured PDF form. Tabular data extraction from reports and other published data in PDF format is of interest for various data consolidation purposes such as analysing and aggregating financial reports of a company. Queries into the structured tabular data in PDF format are normally processed in an unstructured manner through means like text-match. This is mainly due to that the binary format of PDF documents is optimized for layout and rendering and do not have great support for automated parsing of data. Moreover, even the same table type in PDF files varies in schema, row or column headers, which makes it difficult for a query plan to cover all relevant tables. This paper proposes a deep learning based method to enable SQL-like query and analysis of financial tables from annual reports in PDF format. This is achieved through table type classification and nearest row search. We demonstrate that using word embedding trained on Google news for header match clearly outperforms the text-match based approach in traditional database. We also introduce a practical system that uses this technology to query and analyse finance tables in PDF documents from various sources.