Query: Investigating How Student's Cognitive Behavior in MOOC Discussion Forum Affect Learning Gains

Relevant passages:
Question Asking During Tutoring <=SEP=> Whereas it is well documented that student question asking is infrequent in classroom environments, there is little research on questioning processes during tutoring. The present study investigated the questions asked in tutoring sessions on research methods (college students) and algebra (7th graders). Student questions were approximately 240 times as frequent in tutoring settings as classroom settings, whereas tutor questions were only slightly more frequent than teacher questions. Questions were classified by (a) degree of specification , (b) content, and (c) question-generation mechanism to analyze their quality. Student achievement was positively correlated with the quality of student questions after students had some experience with tutoring, but the frequency of questions was not correlated with achievement. Students partially self-regulated their learning by identifying knowledge deficits and asking questions to repair them, but they need training to improve these skills. We identified some ways that tutors and teachers might improve their question-asking skills. specializations are discourse processes and questioning mechanisms.

Sentiment Analysis in MOOC Discussion Forums: What does it tell us? <=SEP=> Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students’ trending opinions towards the course and major course tools, such as lecture and peer-assessment. We observe a correlation between sentiment ratio measured based on daily forum posts and number of students who drop out each day. On a user-level, we evaluate the impact of sentiment on attrition over time. A qualitative analysis clarifies the subtle differences in how these language behaviors are used in practice across three MOOCs. Implications for research and practice are discussed.

Visualizing patterns of student engagement and performance in MOOCs <=SEP=> In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.

Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities <=SEP=> Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited.

Major life changes and behavioral markers in social media: case of childbirth <=SEP=> We explore the harnessing of social media as a window on changes around major life events in individuals and larger populations. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. After identifying childbirth events on Twitter, we analyze daily posting patterns and language usage before and after birth by new mothers, and make inferences about the status and dynamics of changes in emotions expressed following childbirth. We find that childbirth is associated with some changes for most new mothers, but approximately 15% of new mothers show significant changes in their online activity and emotional expression postpartum. We observe that these mothers can be distinguished by linguistic changes captured by shifts in a relatively small number of words in their social media posts. We introduce a greedy differencing procedure to identify the type of language that characterizes significant changes in these mothers during postpartum. We conclude with a discussion about how such characterizations might be applied to recognizing and understanding health and well-being in women following childbirth.

Human microbiomes and their roles in dysbiosis, common diseases, and novel therapeutic approaches <=SEP=> The human body is the residence of a large number of commensal (non-pathogenic) and pathogenic microbial species that have co-evolved with the human genome, adaptive immune system, and diet. With recent advances in DNA-based technologies, we initiated the exploration of bacterial gene functions and their role in human health. The main goal of the human microbiome project is to characterize the abundance, diversity and functionality of the genes present in all microorganisms that permanently live in different sites of the human body. The gut microbiota expresses over 3.3 million bacterial genes, while the human genome expresses only 20 thousand genes. Microbe gene-products exert pivotal functions via the regulation of food digestion and immune system development. Studies are confirming that manipulation of non-pathogenic bacterial strains in the host can stimulate the recovery of the immune response to pathogenic bacteria causing diseases. Different approaches, including the use of nutraceutics (prebiotics and probiotics) as well as phages engineered with CRISPR/Cas systems and quorum sensing systems have been developed as new therapies for controlling dysbiosis (alterations in microbial community) and common diseases (e.g., diabetes and obesity). The designing and production of pharmaceuticals based on our own body's microbiome is an emerging field and is rapidly growing to be fully explored in the near future. This review provides an outlook on recent findings on the human microbiomes, their impact on health and diseases, and on the development of targeted therapies.

Human motion recovery utilizing truncated schatten p-norm and kinematic constraints <=SEP=> Human motion capture (mocap) data, which records the movements of joints of a human body, has been widely used in many areas. However, the raw captured data inevitably contains missing data due to the limitations of capture systems. Recently, by exploiting the low-rank prior embedded in mocap data, several approaches have resorted to the low-rank matrix completion (LRMC) to fill in the missing data and obtained encouraging results. In order to solve the resulting rank minimization problem which is known as NP-hard, all existing methods use the convex nuclear norm as the surrogate of rank to pursue the convexity of the objective function. However, the nuclear norm, which ignores physical interpretations of singular values and has over-shrinking problem, obtains less accurate approximation than its nonconvex counterpart. Therefore, this paper presents a nonconvex LRMC based method wherein we exploit the state-of-art nonconvex truncated schattenp norm to approximate rank. Moreover, we add two significant constraints to the low-rank based model to preserve the spatial-temporal properties and structural characteristics embedded in human motion. We also develop a framework based on alternating direction multiplier method (ADMM) to solve the resulting nonconvex problem. Extensive experiment results demonstrate that the proposed method significantly outperforms the existing state-of-art methods in terms of both recovery error and agreement with human intuition. © 2018 Elsevier Inc. All rights reserved.

Self-Adjusting Binary Search Trees <=SEP=> The splay tree, a self-adjusting form of binary search tree, is developed and analyzed. The binary search tree is a data structure for representing tables and lists so that accessing, inserting, and deleting items is easy. On an n-node splay tree, all the standard search tree operations have an amortized time bound of O(log n) per operation, where by “amortized time” is meant the time per operation averaged over a worst-case sequence of operations. Thus splay trees are as efficient as balanced trees when total running time is the measure of interest. In addition, for sufficiently long access sequences, splay trees are as efficient, to within a constant factor, as static optimum search trees. The efficiency of splay trees comes not from an explicit structural constraint, as with balanced trees, but from applying a simple restructuring heuristic, called splaying, whenever the tree is accessed. Extensions of splaying give simplified forms of two other data structures: lexicographic or multidimensional search trees and link/cut trees.

Minimal intervention dentistry: part 4. Detection and diagnosis of initial caries lesions <=SEP=> The detection of carious lesions is focused on the identification of early mineral changes to allow the demineralisation process to be managed by non-invasive interventions. The methods recommended for clinical diagnosis of initial carious lesions are discussed and illustrated. These include the early detection of lesions, evaluation of the extent of the lesion and its state of activity and the establishment of appropriate monitoring. The place of modern tools, including those based on fluorescence, is discussed. These can help inform patients. They are also potentially useful in regular control visits to monitor the progression or regression of early lesions. A rigorous and systematic approach to caries diagnosis is essential to establish a care plan for the disease and to identify preventive measures based on more precise diagnosis and to reduce reliance on restorative measures.

Guidelines for usability testing with children <=SEP=> A Although user-centered design is a well-supported concept in the literature on adult computer products, not until recently have publications begun to appear addressing the need to include the user in the design process of children's computer products. Good examples are a recent panel discussion in interactions on the importance of understanding the perspectives and needs of children, and the energizing work of Allison Druin and Cynthia Solomon [1, 2]. Growth has also occurred in evaluation research in both the industrial and academic communities, assessing the effectiveness or appeal of various types of (6 to 10 years), and middle-school-aged children (11 to 14 years). These age divisions are arbitrary , and many behaviors will overlap. In our experience, most children younger than 2 1/2 years of age are not proficient enough with standard input devices (e.g., mouse, trackball, or keyboard) to interact with the technology and provide useful data. Children older than 14 years of age will likely behave as adults in a testing situation and should be treated accordingly. Preschool (ages 2 to 5 years) Preschoolers require the most extensive adaptations of usability testing because their attention span, their motivation to please adults, and their ability to adjust to strange surroundings and new people may change from one moment to the next. In general, children in this age range should be allowed to explore the computer according to their own interests and pacing instead of performing a series of directed tasks. They will often be happy to show you what they know, and what they can do on the computer independently. When assessing appeal or engagement, testers will need to closely observe children's behavior such as sighing, smiling, or sliding under the table. Children this age often have difficulty expressing their likes and dislikes in words. Elementary School (ages 6 to 10 years) Children in this age range are relatively easy to include in software usability testing. Their experience in school makes them ready to sit at a task and follow directions from an adult, and they are generally not self-conscious about being observed as they play on the computer. They will answer questions and try new things with ease. In this age range, children will develop more sophistication about how they can describe the things they see and do. Six-and seven-year-old children will be more hands-on—ready to work on the computer but a little shy or inarticulate when …

Design of Kernels in Convolutional Neural Networks for Image Classification <=SEP=> Despite the effectiveness of convolutional neural networks (CNNs) for image classification, our understanding of the effect of shape of convolution kernels on learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define receptive fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we present a feature visualization method for visualization of pixel-wise classification score maps of learned features. Motivated by our experimental results, and observations reported in the literature for modeling of visual systems, we propose a novel design of shape of kernels for learning of representations in CNNs. In the experimental results, the proposed models achieved an outstanding performance in the classification task, comparing to a base CNN models that introduces more parameters and computational time. Additionally, we examined the region of interest (ROI) of different models in the classification task and analyzed the robustness of the proposed method to occluded images. Our results indicate the effectiveness of the proposed approach.

4D Light Field Superpixel and Segmentation <=SEP=> Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate the ambiguity. The LFSP is first defined mathematically and then a refocus-invariant metric named LFSP self-similarity is proposed to evaluate the segmentation performance. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the state-of-the-arts in terms of traditional evaluation metrics. Additionally the LFSP self-similarity evaluation under different light field refocus levels shows the refocus-invariance of the proposed algorithm.

Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors <=SEP=> Multi-agent learning provides a potential framework for learning and simulating traffic behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a traffic scenario. The proposed architecture can learn multiple behaviors independently as well as simultaneously. We take advantage of the homogeneity of agents and learn in a parameter sharing paradigm. To further speed up the training process asynchronous updates are employed into the architecture. While learning different behaviors simultaneously, the given framework was also able to learn cooperation between the agents, without any explicit communication. We applied this framework to learn two important behaviors in driving: 1) Lane-Keeping and 2) Over-Taking. Results indicate faster convergence and learning of a more generic behavior, that is scalable to any number of agents. When compared the results with existing approaches, our results indicate equal and even better performance in some cases.

HarmTrace : Automatic functional harmonic analysis <=SEP=> Music scholars have been intensively studying tonal harmony for centuries, yielding numerous theories and models. Unfortunately, a large number of these theories are formulated in a rather informal fashion and lack mathematical precision. In this article we present HarmTrace, a functional model of Western tonal harmony, which builds on well-known theories of tonal harmony. In contrast to many other theories which remain purely theoretical, we present an implemented system that is evaluated empirically. Given a sequence of symbolic chord labels, HarmTrace automatically derives the harmonic relations between chords. For this, we use advanced functional programming techniques which are uniquely available in the Haskell programming language. We show that our system is fast, easy to modify and maintain, is robust against noisy data, and returns harmonic analyses that comply with Western tonal harmony theory.

Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks <=SEP=> Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.

Snooping Keystrokes with mm-level Audio Ranging on a Single Phone <=SEP=> This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.

On Best-Possible Obfuscation <=SEP=> An obfuscator is a compiler that transforms any program (which we will view in this work as a boolean circuit) into an obfuscated program (also a circuit) that has the same input-output functionality as the original program, but is “unintelligible”. Obfuscation has applications for cryptography and for software protection. Barak et al. (CRYPTO 2001, pp. 1–18, 2001) initiated a theoretical study of obfuscation, which focused on black-box obfuscation, where the obfuscated circuit should leak no information except for its (black-box) input-output functionality. A family of functionalities that cannot be obfuscated was demonstrated. Subsequent research has showed further negative results as well as positive results for obfuscating very specific families of circuits, all with respect to black box obfuscation. This work is a study of a new notion of obfuscation, which we call best-possible obfuscation. Best possible obfuscation makes the relaxed requirement that the obfuscated program leaks as little information as any other program with the same functionality (and of similar size). In particular, this definition allows the program to leak information that cannot be obtained from a black box. Best-possible obfuscation guarantees that any information that is not hidden by the obfuscated program is also not hidden by any other similar-size program computing the same functionality, and thus the obfuscation is (literally) the best possible. In this work we study best-possible obfuscation and its relationship to previously studied definitions. Our main results are: (1) A separation between black-box and best-possible obfuscation. We show a natural obfuscation task that can be achieved under the best-possible definition, but cannot be achieved under the black-box definition. (2) A hardness result for best-possible obfuscation, showing that strong (information-theoretic) best-possible obfuscation implies a collapse in the Polynomial-Time Hierarchy. (3) An impossibility result for efficient best-possible (and black-box) obfuscation in the presence of random oracles. This impossibility result uses a random oracle to construct hard-to-obfuscate circuits, and thus it does not imply impossibility in the standard model.

Main-memory foreign key joins on advanced processors: design and re-evaluations for OLAP workloads <=SEP=> The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the surrogate key index oriented foreign key join as schema-conscious and OLAP workload customized design foreign key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign key join algorithm with surrogate key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables surrogate key index to be effective for foreign key joins in data warehousing workloads for both CPU and hardware accelerators.

Electricity-producing bacterial communities in microbial fuel cells. <=SEP=> Microbial fuel cells (MFCs) are not yet commercialized but they show great promise as a method of water treatment and as power sources for environmental sensors. The power produced by these systems is currently limited, primarily by high internal (ohmic) resistance. However, improvements in the system architecture will soon result in power generation that is dependent on the capabilities of the microorganisms. The bacterial communities that develop in these systems show great diversity, ranging from primarily delta-Proteobacteria that predominate in sediment MFCs to communities composed of alpha-, beta-, gamma- or delta-Proteobacteria, Firmicutes and uncharacterized clones in other types of MFCs. Much remains to be discovered about the physiology of these bacteria capable of exocellular electron transfer, collectively defined as a community of "exoelectrogens". Here, we review the microbial communities found in MFCs and the prospects for this emerging bioenergy technology.

A survey on ontology mapping <=SEP=> Ontology is increasingly seen as a key factor for enabling interoperability across heterogeneous systems and semantic web applications. Ontology mapping is required for combining distributed and heterogeneous ontologies. Developing such ontology mapping has been a core issue of recent ontology research. This paper presents ontology mapping categories, describes the characteristics of each category, compares these characteristics, and surveys tools, systems, and related work based on each category of ontology mapping. We believe this paper provides readers with a comprehensive understanding of ontology mapping and points to various research topics about the specific roles of ontology mapping.

Managing Risk in Software Process Improvement: An Action Research Approach <=SEP=> Many software organizations engage in software process improvement (SPI) initiatives to increase their capability to develop quality solutions at a competitive level. Such efforts, however, are complex and very demanding. A variety of risks makes it difficult to develop and implement new processes. We studied SPI in its organizational context through collaborative practice research (CPR), a particular form of action research. The CPR program involved close collaboration between practitioners and researchers over a three-year period to understand and improve SPI initiatives in four Danish software organizations. The problem of understanding and managing risks in SPI teams emerged in one of the participating organizations and led to this research. We draw upon insights from the literature on SPI and software risk management as well as practical lessons learned from managing SPI risks in the participating software organizations. Our research offers two contributions. First, we contribute to knowledge on SPI by proposing an approach to understand and manage risks in SPI teams. This risk management approach consists of a framework for understanding risk areas and risk resolution strategies within SPI and a related Iversen et al./Managing Risk in SPI 396 MIS Quarterly Vol. 28 No. 3/September 2004 process for managing SPI risks. Second, we contribute to knowledge on risk management within the information systems and software engineering disciplines. We propose an approach to tailor risk management to specific contexts. This approach consists of a framework for understanding and choosing between different forms of risk management and a process to tailor risk management to specific contexts.

Radial-arrayed rotary electrification for high performance triboelectric generator. <=SEP=> Harvesting mechanical energy is an important route in obtaining cost-effective, clean and sustainable electric energy. Here we report a two-dimensional planar-structured triboelectric generator on the basis of contact electrification. The radial arrays of micro-sized sectors on the contact surfaces enable a high output power of 1.5 W (area power density of 19 mW cm(-2)) at an efficiency of 24%. The triboelectric generator can effectively harness various ambient motions, including light wind, tap water flow and normal body movement. Through a power management circuit, a triboelectric-generator-based power-supplying system can provide a constant direct-current source for sustainably driving and charging commercial electronics, immediately demonstrating the feasibility of the triboelectric generator as a practical power source. Given exceptional power density, extremely low cost and unique applicability resulting from distinctive mechanism and structure, the triboelectric generator can be applied not only to self-powered electronics but also possibly to power generation at a large scale.

CATCH: A detecting algorithm for coalition attacks of hit inflation in internet advertising <=SEP=> As the Internet flourishes, online advertising becomes essential for marketing campaigns for business applications. To perform a marketing campaign, advertisers provide their advertisements to Internet publishers and commissions are paid to the publishers of the advertisements based on the clicks made for the posted advertisements or the purchases of the products of which advertisements posted. Since the payment given to a publisher is proportional to the amount of clicks received for the advertisements posted by the publisher, dishonest publishers are motivated to inflate the number of clicks on the advertisements hosted on their web sites. Since the click frauds are critical for online advertising to be reliable, the online advertisers make the efforts to prevent them effectively. However, the methods used for click frauds are also becoming more complex and sophisticated. In this paper, we study the problem of detecting coalition attacks of click frauds. The coalition attacks of click fraud is one of the latest sophisticated techniques utilized for click frauds because the fraudsters can obtain not only more gain but also less probability of being detected by joining a coalition. We introduce new definitions for the coalition and propose the novel algorithm called CATCH to find such coalitions. Extensive experiments with synthetic and real-life data sets confirm that our notion of coalition allows us to detect coalitions much more effectively than that of

FPGA Implementations of the AES Masked Against Power Analysis Attacks <=SEP=> Power analysis attacks are a serious treat for implementations of modern cryptographic algorithms. Masking is a particularly appealing countermeasure against such attacks since it increases the security to a well quantifiable level and can be implemented without modifying the underlying technology. Its main drawback is the performance overhead it implies. For example, due to prohibitive memory costs, the straightforward application of masking to the AES algorithm, with precomputed tables, is hardly practical. In this paper, we exploit both the increased size of state-of-the-art reconfigurable hardware devices and previous optimization techniques to minimize the memory occupation of software S-boxes, in order to provide an efficient FPGA implementation of the AES algorithm, masked against side-channel attacks. We describe two high throughput architectures, based on 32-bit and 128-bit datapaths that are suitable for Xilinx Virtex-5 devices. In this way, we demonstrate the possibility to efficiently combine technological advances with algorithmic optimizations in this context.

A Topic-based Reviewer Assignment System <=SEP=> Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.

Smooth sensitivity and sampling in private data analysis <=SEP=> We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework allows one to release functions f of the data withinstance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also bythe database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smoothsensitivity of f on the database x --- a measure of variabilityof f in the neighborhood of the instance x. The new frameworkgreatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a smallamount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-basednoise in the context of data privacy.
 Our framework raises many interesting algorithmic questions. Namely,to apply the framework one must compute or approximate the smoothsensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost ofthe minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on manydatabases x. This procedure is applicable even when no efficient algorithm for approximating smooth sensitivity of f is known orwhen f is given as a black box. We illustrate the procedure by applying it to k-SED (k-means) clustering and learning mixtures of Gaussians.

Dynamics and control of a 4-dof wearable cable-driven upper arm exoskeleton <=SEP=> In this paper, we present the dynamics, control, and preliminary experiments on a wearable upper arm exoskeleton intended for human users with four degrees-of-freedom (dof), driven by six cables. The control of this cable-driven exoskeleton is complicated because the cables can transmit forces to the arm only under tension. The standard PD controllers or Computed torque controllers perform only moderately since the cables need to be in tension. Future efforts will seek to refine these control strategies and their implementations to improve functionality of a human user.

Household availability of ultra-processed foods and obesity in nineteen European countries. <=SEP=> OBJECTIVE
To assess household availability of NOVA food groups in nineteen European countries and to analyse the association between availability of ultra-processed foods and prevalence of obesity.


DESIGN
Ecological, cross-sectional study.


SETTING
Europe.


SUBJECTS
Estimates of ultra-processed foods calculated from national household budget surveys conducted between 1991 and 2008. Estimates of obesity prevalence obtained from national surveys undertaken near the budget survey time.


RESULTS
Across the nineteen countries, median average household availability amounted to 33·9 % of total purchased dietary energy for unprocessed or minimally processed foods, 20·3 % for processed culinary ingredients, 19·6 % for processed foods and 26·4 % for ultra-processed foods. The average household availability of ultra-processed foods ranged from 10·2 % in Portugal and 13·4 % in Italy to 46·2 % in Germany and 50·4 % in the UK. A significant positive association was found between national household availability of ultra-processed foods and national prevalence of obesity among adults. After adjustment for national income, prevalence of physical inactivity, prevalence of smoking, measured or self-reported prevalence of obesity, and time lag between estimates on household food availability and obesity, each percentage point increase in the household availability of ultra-processed foods resulted in an increase of 0·25 percentage points in obesity prevalence.


CONCLUSIONS
The study contributes to a growing literature showing that the consumption of ultra-processed foods is associated with an increased risk of diet-related non-communicable diseases. Its findings reinforce the need for public policies and actions that promote consumption of unprocessed or minimally processed foods and make ultra-processed foods less available and affordable.

Variable Rate Image Compression with Recurrent Neural Networks <=SEP=> Although image compression has been actively studied for decades, there has been relatively little research on learning to compress images with modern neural networks. Standard approaches, such as those employing patch-based autoencoders, have shown a great deal of promise but cannot compete with popular image codecs because they fail to address three questions: 1) how to effectively binarize activations: in the absence of binarization, a bottleneck layer alone tends not to lead to efficient compression; 2) how to achieve variable-rate encoding: a standard autoencoder generates a fixed-length code for each fixed-resolution input patch, resulting in the same cost for lowand high-entropy patches, and requiring the network to be completely retrained to achieve different compression rates; and 3) how to avoid block artifacts: patch-based approaches are prone to block discontinuities. We propose a general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional recurrent networks, including LSTMs, that address these issues and report promising results compared to existing baseline codecs. We evaluate the proposed methods on a large-scale benchmark consisting of tiny images (32× 32), which proves to be very challenging for all the methods.

Predicate learning in neural systems: Discovering latent generative structures <=SEP=> Humans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, models that infer higher-order structures from sensory or first-order representations have been proposed to account for the complexity and flexibility of human behavior. But how do the structures that these models invoke arise in neural systems in the first place? To answer this question, we explain how a system can learn latent representational structures (i.e., predicates) from experience with wholly unstructured data. During the process of predicate learning, an artificial neural network exploits the naturally occurring dynamic properties of distributed computing across neuronal assemblies in order to learn predicates, but also to combine them compositionally, two computational aspects which appear to be necessary for human behavior as per formal theories in multiple domains. We describe how predicates can be combined generatively using neural oscillations to achieve human-like extrapolation and compositionality in an artificial neural network. The ability to learn predicates from experience, to represent structures compositionally, and to extrapolate to unseen data offers an inroads to understanding and modeling the most complex human behaviors.