Reviewerly/exHarmony huggingface dataset:
An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images

Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term. X-ray mammography is still the instrument of choice in breast cancer screening. In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more. The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation. Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular. Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts. In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only. We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.
Collaborative Problem Solving: Processing Actions, Time, and Performance

This study is based on one collaborative problem solving task from an international assessment: the Xandar task. It was developed and delivered by the Organisation for Economic Co-operation and Development Programme for International Student Assessment (OECD PISA) 2015. We have investigated the relationship of problem solving performance with invested time and number of actions in collaborative episodes for the four parts of the Xandar task. The parts require the respondent to collaboratively plan a process for problem solving, implement the process, reach a solution, and evaluate the solution (For a full description, see the Materials and Methods section, “Parts of the Xandar Task”.) Examples of an action include posting to a chat log, accessing a shared resource, or conducting a search on a map tool. Actions taken in each part of the task were identified by PISA and recorded in the data set numerically. A confirmatory factor analysis model looks at two types of relationship: at the level of latent variables (the factors) and at extra dependencies, which here are direct effects and correlated residuals (independent of the factors). The model, which is well-fitting, has three latent variables: actions (A), times (T), and level of performance (P). Evidence for the uni-dimensionality of performance level is also found in a separate analysis of the binary items. On the whole for the entire task, participants with more activities are less successful and faster, based on the U.S. data set employed in the analysis. By contrast, successful participants take more time. By task part, the model also investigates relationships between activities, time, and performance level within the parts. This was done because one can expect dependencies within parts of such a complex task. Results indicate some general and some specific relationships within the parts, see the full manuscript for more detail. We conclude with a discussion of what the investigated relationships may reveal. We also describe why such investigations may be important to consider when preparing students for improved skills in collaborative problem solving, considered a key aspect of successful 21st century skills in the workplace and in everyday life in many countries.
Technology and Higher Education: A Bibliometric Analysis

Technology in education is one of the current realities that generates most interest. The objective of this research is to analyze the scientific production of educational technology in the higher education stage, within the Web of Science database. To carry out this study, a bibliometric study was conducted, which first established the keywords that would govern the rest of the research and continued with the analysis of different variables that addressed different features, from the origin of the archives to the authors or the most prolific and relevant institutions in the scientific community. The results indicate that there has been a significant increase in publications on this subject in recent years, and as evident from the current peak, this is a growing trend that will lead to more research in the coming years.
Freeze and Chaos for DNNs: an NTK view of Batch Normalization, Checkerboard and Boundary Effects.

In this paper, we analyze a number of architectural features of Deep NeuralNetworks (DNNs), using the so-called Neural Tangent Kernel (NTK). The NTKdescribes the training trajectory and generalization of DNNs in theinfinite-width limit. In this limit, we show that for (fully-connected) DNNs, as the depth grows,two regimes appear: freeze (also known as order), where the (scaled) NTKconverges to a constant (slowing convergence), and chaos, where it convergesto a Kronecker delta (limiting generalization). We show that when using the scaled ReLU as a nonlinearity, we naturally endup in the freeze. We show that Batch Normalization (BN) avoids the freezeregime by reducing the importance of the constant mode in the NTK. A similareffect is obtained by normalizing the nonlinearity which moves the network tothe chaotic regime. We uncover the same freeze and chaos modes in Deep DeconvolutionalNetworks (DC-NNs). The freeze regime is characterized by checkerboardpatterns in the image space in addition to the constant modes in input space.Finally, we introduce a new NTK-based parametrization to eliminate borderartifacts and we propose a layer-dependent learning rate to improve theconvergence of DC-NNs. We illustrate our findings by training DCGANs using our setup. When trainedin the freeze regime, we see that the generator collapses to a checkerboardmode. We also demonstrate numerically that the generator collapse can beavoided and that good quality samples can be obtained, by tuning thenonlinearity to reach the chaos regime (without using batch normalization).
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool

In recent years, visual question answering (VQA) has become topical. The premise of VQA's significance as a benchmark in AI, is that both the image and textual question need to be well understood and mutually grounded in order to infer the correct answer. However, current VQA models perhaps `understand' less than initially hoped, and instead master the easier task of exploiting cues given away in the question and biases in the answer distribution [1] . In this paper we propose the inverse problem of VQA (iVQA). The iVQA task is to generate a question that corresponds to a given image and answer pair. We propose a variational iVQA model that can generate diverse, grammatically correct and content correlated questions that match the given answer. Based on this model, we show that iVQA is an interesting benchmark for visuo-linguistic understanding, and a more challenging alternative to VQA because an iVQA model needs to understand the image better to be successful. As a second contribution, we show how to use iVQA in a novel reinforcement learning framework to diagnose any existing VQA model by way of exposing its belief set: the set of question-answer pairs that the VQA model would predict true for a given image. This provides a completely new window into what VQA models `believe' about images. We show that existing VQA models have more erroneous beliefs than previously thought, revealing their intrinsic weaknesses. Suggestions are then made on how to address these weaknesses going forward.
Measures of the Benefit of Direct Encoding of Data Deltas for Data Pair Relation Perception

The power of data visualization is not to convey absolute values of individual data points, but to allow the exploration of relations (increases or decreases in a data value) among them. One approach to highlighting these relations is to explicitly encode the numeric differences (deltas) between data values. Because this approach removes the context of the individual data values, it is important to measure how much of a performance improvement it actually offers, especially across differences in encodings and tasks, to ensure that it is worth adding to a visualization design. Across 3 different tasks, we measured the increase in visual processing efficiency for judging the relations between pairs of data values, from when only the values were shown, to when the deltas between the values were explicitly encoded, across position and length visual feature encodings (and slope encodings in Experiments 1 & 2). In Experiment 1, the participant's task was to locate a pair of data values with a given relation (e.g., Find the 'small bar to the left of a tall bar' pair) among pairs of the opposite relation, and we measured processing efficiency from the increase in response times as the number of pairs increased. In Experiment 2, the task was to judge which of two relation types was more prevalent in a briefly presented display of 10 data pairs (e.g., Are there more 'small bar to the left of a tall bar' pairs or more 'tall bar to the left of a small bar' pairs?). In the final experiment, the task was to estimate the average delta within a briefly presented display of 6 data pairs (e.g., What is the average bar height difference across all 'small bar to the left of a tall bar' pairs?). Across all three experiments, visual processing of relations between data value pairs was significantly better when directly encoded as deltas rather than implicitly between individual data points, and varied substantially depending on the task (improvement ranged from 25% to 95%). Considering the ubiquity of bar charts and dot plots, relation perception for individual data values is highly inefficient, and confirms the need for alternative designs that provide not only absolute values, but also direct encoding of critical relationships between those values.
Deep Feature Selection and Causal Analysis of Alzheimer’s Disease

Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer's disease (AD). However, it is also well known that DCNNs are "black boxes" owing to their low interpretability to humans. The lack of transparency of deep learning compromises its application to the prediction and mechanism investigation in AD. To overcome this limitation, we develop a novel general framework that integrates deep leaning, feature selection, causal inference, and genetic-imaging data analysis for predicting and understanding AD. The proposed algorithm not only improves the prediction accuracy but also identifies the brain regions underlying the development of AD and causal paths from genetic variants to AD via image mediation. The proposed algorithm is applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with diffusion tensor imaging (DTI) in 151 subjects (51 AD and 100 non-AD) who were measured at four time points of baseline, 6 months, 12 months, and 24 months. The algorithm identified brain regions underlying AD consisting of the temporal lobes (including the hippocampus) and the ventricular system.
Mining on Heterogeneous Manifolds for Zero-Shot Cross-Modal Image Retrieval

Most recent approaches for the zero-shot cross-modal image retrieval map images from different modalities into a uniform feature space to exploit their relevance by using a pre-trained model. Based on the observation that manifolds of zero-shot images are usually deformed and incomplete, we argue that the manifolds of unseen classes are inevitably distorted during the training of a two-stream model that simply maps images from different modalities into a uniform space. This issue directly leads to poor cross-modal retrieval performance. We propose a bi-directional random walk scheme to mining more reliable relationships between images by traversing heterogeneous manifolds in the feature space of each modality. Our proposed method benefits from intra-modal distributions to alleviate the interference caused by noisy similarities in the cross-modal feature space. As a result, we achieved great improvement in the performance of the thermal v.s. visible image retrieval task. The code of this paper: https://github.com/fyang93/cross-modal-retrieval
Performance-Aware Speculative Resource Oversubscription for Large-Scale Clusters

It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current centralized approaches to oversubscription suffer from the issue with resource mismatch and fail to take into account other performance requirements, e.g., tail latency. In this article we present ROSE, a new resource management platform capable of conducting performance-aware resource oversubscription. ROSE allows latency-sensitive long-running applications (LRAs) to co-exist with computation-intensive batch jobs. Instead of waiting for resource allocation to be confirmed by the centralized scheduler, job managers in ROSE can independently request to launch speculative tasks within specific machines according to their suitability for oversubscription. Node agents of those machines can however, avoid any excessive resource oversubscription by means of a mechanism for admission control using multi-resource threshold control and performance-aware resource throttle. Experiments show that in case of mixed co-location of batch jobs and latency-sensitive LRAs, the CPU utilization and the disk utilization can reach 56.34 and 43.49 percent, respectively, but the 95th percentile of read latency in YCSB workloads only increases by 5.4 percent against the case of executing the LRAs alone.
Minimalistic Attacks: How Little It Takes to Fool Deep Reinforcement Learning Policies

Recent studies have revealed that neural-network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing every pixel of every frame assuming white-box policy access, in this article, we take a more restrictive view toward adversary generation—with the goal of unveiling the limits of a model’s vulnerability. In particular, we explore minimalistic attacks by defining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">three key settings</i> : 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Black-Box Policy Access</i> : where the attacker only has access to the input (state) and output (action probability) of an RL policy; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fractional-State Adversary</i> : where only several pixels are perturbed, with the extreme case being a single-pixel adversary; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tactically Chanced Attack</i> : where only significant frames are tactically chosen to be attacked. We formulate the adversarial attack by accommodating the three key settings, and explore their potency on six Atari games by examining four fully trained state-of-the-art policies. In Breakout, for example, we surprisingly find that: 1) all policies showcase significant performance degradation by merely modifying 0.01% of the input state and 2) the policy trained by DQN is totally deceived by perturbing only 1% frames.
Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p 0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.
Polar Relative Positional Encoding for Video-Language Segmentation

In this paper, we tackle a challenging task named video-language segmentation. Given a video and a sentence in natural language, the goal is to segment the object or actor described by the sentence in video frames. To accurately denote a target object, the given sentence usually refers to multiple attributes, such as nearby objects with spatial relations, etc. In this paper, we propose a novel Polar Relative Positional Encoding (PRPE) mechanism that represents spatial relations in a ``linguistic'' way, i.e., in terms of direction and range. Sentence feature can interact with positional embeddings in a more direct way to extract the implied relative positional relations. We also propose parameterized functions for these positional embeddings to adapt real-value directions and ranges. With PRPE, we design a Polar Attention Module (PAM) as the basic module for vision-language fusion. Our method outperforms previous best method by a large margin of 11.4% absolute improvement in terms of mAP on the challenging A2D Sentences dataset. Our method also achieves competitive performances on the J-HMDB Sentences dataset.
Autocorrelation of gradients based violence detection in surveillance videos

Automated monitoring of videos is becoming mandatory due to its widespread applications over public and private domains. Especially, research over detecting anomalous human behavior in crowded scenes has created much attention among computer vision researchers. Understanding patterns in crowded scenes is always challenging due to the rapid movement of the crowd, occlusions and cluttered backgrounds. In this work, we explore spatio-temporal autocorrelation of gradient-based features to efficiently recognize violent activities in crowded scenes. A discriminative classifier is then used to recognize violent actions in videos. Experimental results have shown improved performance of the proposed approach when compared to existing state-of-art-approaches.
Effects of digital learning skill training on the academic performance of undergraduates in science and mathematics.

Many science, engineering, technology, and math (STEM) majors fail to complete their degrees, and those who leave report they lack learning skills required for STEM coursework. In 2 studies, we examined the effects on students’ exam performances when they were assigned to complete a brief digital learning skills training program we embedded into their course site on the university learning management system for their large lecture science and math courses. Study 1 examined whether delivering brief trainings that teach learning skills to students directly within their STEM course site during the first weeks of class would encourage undergraduate science learners to adopt effective learning behaviors and improve their achievement on exams. Additional analyses examined benefits to a group underrepresented in the STEM workforce: first-generation college students. Students who spent time completing training made greater use of resources supporting planning, monitoring, and cognitive strategy use and outperformed students in the control group on initial and final exams. Study 2 examined whether the same, domain-general learning skill training could produce effects when placed in a college algebra course. Random assignment to treatment and control was conducted early in the second course unit. Those in the skill training condition outperformed a control group who solved additional algebra problems on the next 2 unit exams. Across studies, effects of training were similar for first-generation and continuing generation students. However, the effects obtained often improved the exam scores of first-generation students from below to above the minimum score which requires course reenrollment and delays STEM degree completion. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Leveraging blockchain for immutable logging and querying across multiple sites

Abstract Background Blockchain has emerged as a decentralized and distributed framework that enables tamper-resilience and, thus, practical immutability for stored data. This immutability property is important in scenarios where auditability is desired, such as in maintaining access logs for sensitive healthcare and biomedical data. However, the underlying data structure of blockchain, by default, does not provide capabilities to efficiently query the stored data. In this investigation, we show that it is possible to efficiently run complex audit queries over the access log data stored on blockchains by using additional key-value stores. This paper specifically reports on the approach we designed for the blockchain track of iDASH Privacy &amp; Security Workshop 2018 competition. In this track, participants were asked to devise an efficient way to run conjunctive equality and range queries on a genomic dataset access log trail after storing it in a permissioned blockchain network consisting of 4 identical nodes, each representing a different site, created with the Multichain platform. Methods Multichain duplicates and indexes blockchain data locally at each node in a key-value store to support retrieval requests at a later point in time. To efficiently leverage the key-value storage mechanism, we applied various techniques and optimizations, such as bucketization, simple data duplication and batch loading by accounting for the required query types of the competition and the interface provided by Multichain. Particularly, we implemented our solution and compared its loading and query-response performance with SQLite, a commonly used relational database, using the data provided by the iDASH 2018 organizers. Results Depending on the query type and the data size, the run time difference between blockchain based query-response and SQLite based query-response ranged from 0.2 seconds to 6 seconds. A deeper inspection revealed that range queries were the bottleneck of our solution which, nevertheless, scales up linearly. Conclusions This investigation demonstrates that blockchain-based systems can provide reasonable query-response times to complex queries even if they only use simple key-value stores to manage their data. Consequently, we show that blockchains may be useful for maintaining data with auditability and immutability requirements across multiple sites.