Miracle-dz/UniFAR-FTUs huggingface dataset:
Threats to Online Advertising and Countermeasures

Online advertising, also known as web advertising or Internet marketing, is the means and process of promoting products and services on the Internet, and it has been one of the important business models for the Internet. Due to its lucrative nature and its large scale of adoption, it has also been a target for malicious parties with various attack aims such as getting a cut of online advertising revenues, obtaining a user’s privacy, and spreading malware. Over the years, a great deal of research has been conducted on online advertising. Recently, the health of the online advertising ecosystem has become more of a concern for both advertisers and regular Internet users. Advertising budgets have been abused, and Internet users’ privacy and security have been infringed. In this article, we broadly study threats to online advertising and trace the root causes from a systems point of view. Existing threat mitigation strategies are also reviewed and analyzed. To protect online advertising, which has been an essential funding source of many free Internet services, several challenges still need to be addressed, including the need for transparency of the advertising ecosystem and software vulnerabilities on the client-side. To overcome these challenges, we conclude by brainstorming some innovative ideas on some potentially interesting and useful research directions.
Robot-Assisted Tower Construction—A Method to Study the Impact of a Robot’s Allocation Behavior on Interpersonal Dynamics and Collaboration in Groups

Research on human-robot collaboration or human-robot teaming, has focused predominantly on understanding and enabling collaboration between a single robot and a single human. Extending human-robot collaboration research beyond the dyad, raises novel questions about how a robot should allocate resources among group members and about what the consequences of such allocation are for a group’s social dynamics and outcomes. Methodological advances are needed to answer these questions allow researchers to collect data about a robot’s impact not only on interactions with the robot but also on interactions of people with each other. This paper presents Robot Assisted Tower Construction, a novel task that allows researchers to examine the impact of a robot’s allocation behavior on the dynamics of a group or team collaborating on a task. By focusing on the question of whether and how a robot’s allocation of resources (wooden blocks required for a building task) affects collaboration dynamics and outcomes, a case is provided of how this task can be applied in a laboratory study with 124 participants to collect data about human robot collaboration that involves a group of people. We highlight the kinds of insights the task can yield and how it can be adapted to various human robot collaboration contexts.
A Survey of Machine Learning Approaches for Student Dropout Prediction in Online Courses

The recent diffusion of online education (both MOOCs and e-courses) has led to an increased economic and scientific interest in e-learning environments. As widely documented, online students have a much higher chance of dropping out than those attending conventional classrooms. It is of paramount interest for institutions, students, and faculty members to find more efficient methodologies to mitigate withdrawals. Following the rise of attention on the Student Dropout Prediction (SDP) problem, the literature has witnessed a significant increase in contributions to this subject. In this survey, we present an in-depth analysis of the state-of-the-art literature in the field of SDP, under the central perspective, but not exclusive, of machine learning predictive algorithms. Our main contributions are the following: (i) we propose a comprehensive hierarchical classification of existing literature that follows the workflow of design choices in the SDP; (ii) to facilitate the comparative analysis, we introduce a formal notation to describe in a uniform way the alternative dropout models investigated by the researchers in the field; (iii) we analyse some other relevant aspects to which the literature has given less attention, such as evaluation metrics, gathered data, and privacy concerns; (iv) we pay specific attention to deep sequential machine learning methods—recently proposed by some contributors—which represent one of the most effective solutions in this area. Overall, our survey provides novice readers who address these topics with practical guidance on design choices, as well as directs researchers to the most promising approaches, highlighting current limitations and open challenges in the field.
Incremental Discovery of Imprecise Functional Dependencies

Functional dependencies (fds) are one of the metadata used to assess data quality and to perform data cleaning operations. However, to pursue robustness with respect to data errors, it has been necessary to devise imprecise versions of functional dependencies, yielding relaxed functional dependencies (rfds). Among them, there exists the class of rfds relaxing on the extent, i.e., those admitting the possibility that an fd holds on a subset of data. In the literature, several algorithms to automatically discover rfds from big data collections have been defined. They achieve good performances with respect to the inherent problem complexity. However, most of them are capable of discovering rfds only by batch processing the entire dataset. This is not suitable in the era of big data, where the size of a database instance can grow with high-velocity, and the insertion of new data can invalidate previously holding rfds. Thus, it is necessary to devise incremental discovery algorithms capable of updating the set of holding rfds upon data insertions, without processing the entire dataset. To this end, in this article we propose an incremental discovery algorithm for rfds relaxing on the extent. It manages the validation of candidate rfds and the generation of possibly new rfd candidates upon the insertion of the new tuples, while limiting the size of the overall search space. Experimental results show that the proposed algorithm achieves extremely good performances on real-world datasets.
Runtime Design Space Exploration and Mapping of DCNNs for the Ultra-Low-Power Orlando SoC

Recent trends in deep convolutional neural networks (DCNNs) impose hardware accelerators as a viable solution for computer vision and speech recognition. The Orlando SoC architecture from STMicroelectronics targets exactly this class of problems by integrating hardware-accelerated convolutional blocks together with DSPs and on-chip memory resources to enable energy-efficient designs of DCNNs. The main advantage of the Orlando platform is to have runtime configurable convolutional accelerators that can adapt to different DCNN workloads. This opens new challenges for mapping the computation to the accelerators and for managing the on-chip resources efficiently. In this work, we propose a runtime design space exploration and mapping methodology for runtime resource management in terms of on-chip memory, convolutional accelerators, and external bandwidth. Experimental results are reported in terms of power/performance scalability, Pareto analysis, mapping adaptivity, and accelerator utilization for the Orlando architecture mapping the VGG-16, Tiny-Yolo(v2), and MobileNet topologies.
Recurrent Attention Network with Reinforced Generator for Visual Dialog

In Visual Dialog, an agent has to parse temporal context in the dialog history and spatial context in the image to hold a meaningful dialog with humans. For example, to answer “what is the man on her left wearing? ” the agent needs to (1) analyze the temporal context in the dialog history to infer who is being referred to as “her,” (2) parse the image to attend “her,” and (3) uncover the spatial context to shift the attention to “her left” and check the apparel of the man. In this article, we use a dialog network to memorize the temporal context and an attention processor to parse the spatial context. Since the question and the image are usually very complex, which makes it difficult for the question to be grounded with a single glimpse, the attention processor attends to the image multiple times to better collect visual information. In the Visual Dialog task, the generative decoder (G) is trained under the word-by-word paradigm, which suffers from the lack of sentence-level training. We propose to reinforce G at the sentence level using the discriminative model (D), which aims to select the right answer from a few candidates, to ameliorate the problem. Experimental results on the VisDial dataset demonstrate the effectiveness of our approach.
Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs

With the emerging interest in the ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual’s behavioral pattern (e.g., physical activity, location, and mood) by using sensors embedded in smart-watches and smartphones. The multi-sensor environments also come with some challenges, such as how to fuse and combine different sources of data. In this article, we explore several methods of fusion for multi-representations of data from sensors. Furthermore, multiple representations of sensor data were generated and then fused using data-level, feature-level, and decision-level fusions. The presented methods were evaluated using three publicly available human activity recognition (HAR) datasets. The presented approaches utilize Deep Convolutional Neural Networks (CNNs). A generic architecture for fusion of different sensors is proposed. The proposed method shows promising performance, with the best results reaching an overall accuracy of 98.4% for the Context-Awareness via Wrist-Worn Motion Sensors (HANDY) dataset and 98.7% for the Wireless Sensor Data Mining (WISDM version 1.1) dataset. Both results outperform previous approaches.
The Future of False Information Detection on Social Media

The massive spread of false information on social media has become a global risk, implicitly influencing public opinion and threatening social/political development. False information detection (FID) has thus become a surging research topic in recent years. As a promising and rapidly developing research field, we find that much effort has been paid to new research problems and approaches of FID. Therefore, it is necessary to give a comprehensive review of the new research trends of FID. We first give a brief review of the literature history of FID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in FID, which paves a promising way to tackle FID challenges. Finally, we give our views on the open issues and future research directions of FID, such as model adaptivity/generality to new events, embracing of novel machine learning models, aggregation of crowd wisdom, adversarial attack and defense in detection models, and so on.
Translations and Boundaries in the Gap Between HCI Theory and Design Practice

The gap between research and design practice has long been a concern for the HCI community. In this article, we explore how different translations of HCI knowledge might bridge this gap. A literature review characterizes the gap as having two key dimensions—one between general theory and particular artefacts and a second between academic HCI research and professional UX design practice. We report on a 5-year engagement between HCI researchers and a major media company to explore how a particular piece of HCI research, the trajectories conceptual framework, might be translated for and with UX practitioners. We present various translations of this framework and fit them into the gap we previously identified. This leads us to refine the idea of translations, suggesting that they may be led by researchers, by practitioners or co-produced by both as boundary objects. We consider the benefits of each approach.
Troubling Design

The euphemism “female trouble” (discreetly referring to women's health experiences) suggests that trouble is linked to women and bodily transitions women can go through. However, trouble is not only a noun; it is also a verb with a strong feminist tradition. In this article, I present troubling design; a theoretically grounded and practice-oriented design program for designing with women's health. Troubling design brings to matter how trouble is an implicit condition of designing ethical and responsible technologies for women's health. By designing with trouble, troubling design encourages designers and researchers to develop knowledge embedded in criticality and questioning status quo, in order to expand their perspectives on designing with intimate and tabooed bodily experiences. Grounded in feminist theory and research-through-design, troubling design contributes with analytical and generative design knowledge articulated through design examples and three practices: staying with the wrong, curious visiting, and collective imagining.
Identification and characterization of rhizobacteria isolated by enrichment culture on spinach roots

Abstract Rhizobacteria can be used for biological control and environmental restoration. In this study, we performed enrichment culture of rhizobacteria, identified isolates, and investigated the physiological properties of the bacterial isolates. Five bacteria differing in their colony morphology were isolated from spinach roots as enriched rhizobacteria. Four isolates were identified by sequencing of 16S rDNA as β and γ-Proteobacteria; 16S rDNA sequencing was not completed on one isolate. Based on microscopic observation, we determined that at least two types of bacteria differing in their morphology co-existed in this isolate, and that it may not be possible to culture the two types separately. Based on tests of substrate utilization, we could not find the characteristics that were common to the isolates. One of the five isolates was inoculated into non-sterile soil, and we examined its root-colonizing ability. The test strain which was not detected in the non-rhizosphere soil, accounted for about 20% of the total bacteria on the roots. These results suggested that enrichment culture might be useful for isolating bacteria with a high root-colonizing ability
E-Commerce Product Categorization via Machine Translation

E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on machine translation. In our approach, we translate a product’s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products.
Vulvet

Data security and privacy of Android users is one of the challenging security problems addressed by the security research community. A major source of the security vulnerabilities in Android apps is attributed to bugs within source code, insecure APIs, and unvalidated code before performing sensitive operations. Specifically, the major class of app vulnerabilities is related to the categories such as inter-component communication (ICC), networking, web, cryptographic APIs, storage, and runtime-permission validation. A major portion of current contributions focus on identifying a smaller subset of vulnerabilities. In addition, these methods do not discuss how to remove detected vulnerabilities from the affected code. In this work, we propose a novel vulnerability detection and patching framework, Vulvet, which employs static analysis approaches from different domains of program analysis for detection of a wide range of vulnerabilities in Android apps. We propose an additional light-weight technique, FP-Validation, to mitigate false positives in comparison to existing solutions owing to over-approximation. In addition to improved detection, Vulvet provides an automated patching of apps with safe code for each of the identified vulnerability using bytecode instrumentation. We implement Vulvet as an extension of Soot. To demonstrate the efficiency of our proposed framework, we analyzed 3,700 apps collected from various stores and benchmarks consisting of various weak implementations. Our results indicate that Vulvet is able to achieve vulnerability detection with 95.23% precision and 0.975 F-measure on benchmark apps; a significant improvement in comparison to recent works along with successful patching of identified vulnerabilities.
Cosmos+ OpenSSD

As semiconductor technology has advanced, many storage systems have begun to use non-volatile memories as storage media. The organization and architecture of storage controllers have become more complex to meet various design requirements in terms of performance, response time, quality of service (QoS), and so on. In addition, due to the evolution of memory technology and the emergence of new applications, storage controllers employ new firmware algorithms and hardware modules. When designing storage controllers, engineers often evaluate the performance impact of using new software and hardware components using software simulators. However, this technique often yields limited evaluation accuracy because of the difficulty of modeling complex operations of components and the interactions among them. In this article, we present a reconfigurable flash storage controller design that serves as a rapid prototype. This design can be synthesized into a field-programmable gate array device and used in a realistic performance evaluation environment. We show the usefulness of our design by demonstrating the performance impact of design parameters.
SEADS

Distributed software systems are increasingly developed and deployed today. Many of these systems are supposed to run continuously. Given their critical roles in our society and daily lives, assuring the quality of distributed systems is crucial. Analyzing runtime program dependencies has long been a fundamental technique underlying numerous tool support for software quality assurance. Yet conventional approaches to dynamic dependence analysis face severe scalability barriers when they are applied to real-world distributed systems, due to the unbounded executions to be analyzed in addition to common efficiency challenges suffered by dynamic analysis in general. In this article, we present SEADS, a distributed, online, and cost-effective dynamic dependence analysis framework that aims at scaling the analysis to real-world distributed systems. The analysis itself is distributed to exploit the distributed computing resources (e.g., a cluster) of the system under analysis; it works online to overcome the problem with unbounded execution traces while running continuously with the system being analyzed to provide timely querying of analysis results (i.e., runtime dependence set of any given query). Most importantly, given a user-specified time budget, the analysis automatically adjusts itself to better cost-effectiveness tradeoffs (than otherwise) while respecting the budget by changing various analysis parameters according to the time being spent by the dependence analysis. At the core of the automatic adjustment is our application of a reinforcement learning method for the decision making—deciding which configuration to adjust to according to the current configuration and its associated analysis cost with respect to the user budget. We have implemented SEADS for Java and applied it to eight real-world distributed systems with continuous executions. Our empirical results revealed the efficiency and scalability advantages of our framework over a conventional dynamic analysis, at least for dynamic dependence computation at method level. While we demonstrate it in the context of dynamic dependence analysis in this article, the methodology for achieving and maintaining scalability and greater cost-effectiveness against continuously running systems is more broadly applicable to other dynamic analyses.
Identification and Validation of Reference Genes for Quantitative Gene Expression Analysis in Ophraella communa

Ophraella communa is an effective bio-control agent of the invasive common weed. By now, the reference genes in O. communa have not yet been screened and validated. The aim of this study was to screen for the most stable reference genes in different backgrounds, such as different developmental stages, sexes, tissues, and male reproductive system with different body sizes. We selected 12 common housekeeping genes involved in different biological processes, including GAPDH, ACT1, ACT2, ARF1, ARF4, SDH, βTUBC, RPL4, RPL19, RPS18, EF1α, and COX as the candidate reference genes. To analyze the stability of the candidate reference genes, we first used three dedicated algorithms, GeNorm, NormFinder, and BestKeeper, and further comprehensive ranking was provided by ReFinder. The results showed that RPL19 and RPL4 exhibited the least variation in different developmental stages/sexes and in male reproductive systems with different body sizes. COX proved to be most suitable for normalizing the gene expression levels in different tissues, and coincidentally, RPL19 was also found to be second in terms of stability in this study. To the best of our knowledge, this is the first study to identify suitable reference genes for analyzing gene expression in O. communa; thus, this study would lay the foundation for future research on the molecular physiology and biochemistry of O. communa and other insects.
Expression of Human Epidermal Growth Factor Receptor 2 in Meningiomas

Abstract Background: Meningiomas consist of 15%–30% of the primary intracranial tumors, and high-grade meningiomas have a higher recurrence after surgery. The human epidermal growth factor receptor 2 (HER2) status is important in the medical management of patients with various human cancers. This study aimed to evaluate HER2 expression in meningiomas and the correlation between this expression and age, gender, and grade. Materials and Methods: In a descriptive-analytic study, immunohistochemistry (IHC) with HER2 marker was done on confirmed cases of meningioma which were referred to the pathology laboratory from 2008 to 2015. The primary antihuman antibody against c-erbB-2 oncoprotein (DAKO Diagnostics) was used for IHC. Results: Of 117 patients, 68.4% were males. The mean age of the patients was 53.6 years. Grades I, II, and III tumors were 90.6%, 8.5%, and 0.9% of cases, respectively. Totally, 76 (65%) of patients were HER2 positive, with only 7.7% of highly expressed HER2. There was no significant correlation between the mean age (P = 0.672), age group (P = 0.256), sex (P = 0.574), and grade (P = 0.093) and HER2 expression status. Conclusions: We did not find a statistically significant correlation between age, sex, or grade and HER2 status; however, further studies with a higher number of Grades II and III meningiomas and using the fluorescent in situ hybridization in equivocal cases may be of benefit in this way.