Lack of Oversight: The Relationship Between Congress and the FBI, 1907-1975

This study fills a hole left in research about the Federal Bureau of Investigation. While previous authors have examined the Bureau\u27s relationship to the executive branch, especially under its long-time Director, J. Edgar Hoover, comparatively little has been written about the Bureau\u27s relationship with the United States Congress. Using their investigatory and appropriations powers, members of Congress could have maintained stringent oversight of Bureau officials\u27 activities. Instead, members of Congress either deferred to the executive branch, especially presidents and attorneys general, or developed close relationships with Bureau officials based on a shared politics, mainly anti-communism during the Cold War. Examining the relationship from 1907 through 1975 offers numerous examples of members of Congress looking beyond their oversight responsibilities. Even as Congress investigated Bureau actions, no meaningful legislation was passed limiting Bureau activities. Instead, members of Congress left it to the executive branch to correct problems. On issues like wiretapping, Bureau officials either misled Congress about the extent of their activities or ignored Congressional mandates in order to continue their anti- communist agenda. As the Cold War developed, certain Congressional committees began to use Bureau files confidentially in order to educate the public on the dangers of communism. While Bureau officials initially supported such liaison relationships, they were based on the source of the committees\u27 information never coming to light. Once that condition was violated, Bureau officials terminated the relationship, hampering the committees\u27 ability to use the communist issue to further political careers. To fully understand the FBI\u27s role in 20th-century America, the relationship with Congress must be further explored. Focusing solely on Director Hoover or the executive branch is too narrow. Members of Congress had equal opportunity to oversee Bureau activities. That they did not fulfill this responsibility portrays the difficulty Americans have in containing the actions of investigatory agencies

The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study

BackgroundA formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping. ObjectiveThis study aims to demonstrate the feasibility of deep learning technologies for the detection of hand flapping from unstructured home videos as a first step toward validation of whether statistical models coupled with digital technologies can be leveraged to aid in the automatic behavioral analysis of autism. To support the widespread sharing of such home videos, we explored privacy-preserving modifications to the input space via conversion of each video to hand landmark coordinates and measured the performance of corresponding time series classifiers. MethodsWe used the Self-Stimulatory Behavior Dataset (SSBD) that contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From this data set, we extracted 100 hand flapping videos and 100 control videos, each between 2 to 5 seconds in duration. We evaluated five separate feature representations: four privacy-preserved subsets of hand landmarks detected by MediaPipe and one feature representation obtained from the output of the penultimate layer of a MobileNetV2 model fine-tuned on the SSBD. We fed these feature vectors into a long short-term memory network that predicted the presence of hand flapping in each video clip. ResultsThe highest-performing model used MobileNetV2 to extract features and achieved a test F1 score of 84 (SD 3.7; precision 89.6, SD 4.3 and recall 80.4, SD 6) using 5-fold cross-validation for 100 random seeds on the SSBD data (500 total distinct folds). Of the models we trained on privacy-preserved data, the model trained with all hand landmarks reached an F1 score of 66.6 (SD 3.35). Another such model trained with a select 6 landmarks reached an F1 score of 68.3 (SD 3.6). A privacy-preserved model trained using a single landmark at the base of the hands and a model trained with the average of the locations of all the hand landmarks reached an F1 score of 64.9 (SD 6.5) and 64.2 (SD 6.8), respectively. ConclusionsWe created five lightweight neural networks that can detect hand flapping from unstructured videos. Training a long short-term memory network with convolutional feature vectors outperformed training with feature vectors of hand coordinates and used almost 900,000 fewer model parameters. This study provides the first step toward developing precise deep learning methods for activity detection of autism-related behaviors

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

BackgroundAutomated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. ObjectiveWe designed a strategy to gamify the collection and labeling of child emotion–enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. MethodsWe leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion–centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. ResultsThe classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining “anger” and “disgust” into a single class. ConclusionsThis work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts

Overall survival in the OlympiA phase III trial of adjuvant olaparib in patients with germline pathogenic variants in BRCA1/2 and high-risk, early breast cancer

International audienc