Enhanced related-key differential neural distinguishers for SIMON and SIMECK block ciphers.

At CRYPTO 2019, Gohr pioneered the application of deep learning to differential cryptanalysis and successfully attacked the 11-round NSA block cipher Speck32/64 with a 7-round and an 8-round single-key differential neural distinguisher. Subsequently, Lu et al. (DOI 10.

A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis.

One of the first steps in many text-based social science studies is to retrieve documents that are relevant for an analysis from large corpora of otherwise irrelevant documents. The conventional approach in social science to address this retrieval task is to apply a set of keywords and to consider those documents to be relevant that contain at least one of the keywords. But the application of incomplete keyword lists has a high risk of drawing biased inferences.

Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface.

This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications.

Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa).

The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. This study takes an innovative approach to palaeography-the study of ancient handwriting-as a new entry point to access this scribal culture. One of the problems of palaeography is to determine writer identity or difference when the writing style is near uniform.

A Comprehensive Survey on Graph Neural Networks.

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects.

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.

Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data.

Affective Working Memory: An Integrative Psychological Construct.

When people ruminate about an unfortunate encounter with a loved one, savor a long-sought accomplishment, or hold in mind feelings from a marvelous or regretfully tragic moment, what mental processes orchestrate these psychological phenomena? Such experiences typify how affect interacts with working memory, which we posit can occur in three primary ways: emotional experiences can modulate working memory, working memory can modulate emotional experiences, and feelings can be the mental representations maintained by working memory. We propose that this last mode constitutes distinct neuropsychological processes that support the integration of particular cognitive and affective processes: . Accumulating behavioral and neural evidence suggests that affective working memory processes maintain feelings and are partially separable from their cognitive working memory counterparts.

An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study.

Background: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems.

Hadamard single-pixel imaging versus Fourier single-pixel imaging.

Single-pixel imaging which employs active illumination to acquire spatial information is an innovative imaging scheme and has received increasing attentions in recent years. It is applicable to imaging at non-visible wavelengths and imaging under low light conditions. However, single-pixel imaging has once encountered problems of low reconstruction quality and long data-acquisition time.

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments.

Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced.

Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial.

Background: Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time.

Objective: The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression.

Embodied Conversational Agents in Clinical Psychology: A Scoping Review.

Background: Embodied conversational agents (ECAs) are computer-generated characters that simulate key properties of human face-to-face conversation, such as verbal and nonverbal behavior. In Internet-based eHealth interventions, ECAs may be used for the delivery of automated human support factors.

Objective: We aim to provide an overview of the technological and clinical possibilities, as well as the evidence base for ECA applications in clinical psychology, to inform health professionals about the activity in this field of research.

Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data.

Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data.

Utilising dog-computer interactions to provide mental stimulation in dogs especially during ageing.

Aged dogs suffer from reduced mobility and activity levels, which can affect their daily lives. It is quite typical for owners of older dogs to reduce all activities such as walking, playing and training, since their dog may appear to no longer need them. Previous studies have shown that ageing can be slowed by mental and physical stimulation, and thus stopping these activities might actually lead to faster ageing in dogs, which can result in a reduction in the quality of life of the animal, and may even decrease the strength of the dog-owner bond.

From damage to discovery via virtual unwrapping: Reading the scroll from En-Gedi.

Computer imaging techniques are commonly used to preserve and share readable manuscripts, but capturing writing locked away in ancient, deteriorated documents poses an entirely different challenge. This software pipeline-referred to as "virtual unwrapping"-allows textual artifacts to be read completely and noninvasively. The systematic digital analysis of the extremely fragile En-Gedi scroll (the oldest Pentateuchal scroll in Hebrew outside of the Dead Sea Scrolls) reveals the writing hidden on its untouchable, disintegrating sheets.

Smartphone-Based Conversational Agents and Responses to Questions About Mental Health, Interpersonal Violence, and Physical Health.

Importance: Conversational agents are smartphone-based computer programs designed to respond to users in natural language, thereby mimicking conversations between people. Many people use their smartphones to obtain health information.

Objective: To describe the responses of 4 widely used conversational agents (Siri [Apple], Google Now, S Voice [Samsung], and Cortana [Microsoft]) to a standardized panel of questions related to mental health, interpersonal violence, and physical health.

Deep learning.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer.

Openness to Experience: its lower level structure, measurement, and cross-cultural equivalence.

Openness to Experience is an important but relatively poorly understood personality construct. Advances in openness research require further construct clarification as well as establishment of a common framework for conceptualizing and measuring the lower level structure of the construct. In this article, we present data from 3 studies to address this research need.

What is the evidence that poster presentations are effective in promoting knowledge transfer? A state of the art review.

Background: Poster presentations are a common form of presenting health information at conferences and in the community. Anecdotal evidence within the discipline indicates that health information framed in a poster presentation may be an effective method of knowledge transfer.

Objectives: A state of the art review of the literature was performed to determine the effectiveness of poster presentations on knowledge transfer.

The graph neural network model.

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs.

Inferential reasoning by exclusion in pigeons, dogs, and humans.

The ability to reason by exclusion (which is defined as the selection of the correct alternative by logically excluding other potential alternatives; Call in Anim Cogn 9:393-403 2006) is well established in humans. Several studies have found it to be present in some nonhuman species as well, whereas it seems to be somewhat limited or even absent in others. As inconsistent methodology might have contributed to the revealed inter-species differences, we examined reasoning by exclusion in pigeons (n = 6), dogs (n = 6), students (n = 6), and children (n = 8) under almost equal experimental conditions.

Test-enhanced learning: taking memory tests improves long-term retention.

Taking a memory test not only assesses what one knows, but also enhances later retention, a phenomenon known as the testing effect. We studied this effect with educationally relevant materials and investigated whether testing facilitates learning only because tests offer an opportunity to restudy material. In two experiments, students studied prose passages and took one or three immediate free-recall tests, without feedback, or restudied the material the same number of times as the students who received tests.

Region filling and object removal by exemplar-based image inpainting.

A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) "texture synthesis" algorithms for generating large image regions from sample textures and 2) "inpainting" techniques for filling in small image gaps.

Rh(II) and Rh(I) two-legged piano-stool complexes: structure, reactivity, and electronic properties.

The ligand 1,4-bis[4-(diphenylphosphino)butyl]-2,3,5,6-tetramethylbenzene, 3, was used to synthesize a mononuclear Rh(II) complex [(eta(1):eta(6):eta(1)-1,4-bis[4-(diphenylphosphino)butyl]-2,3,5,6-tetramethylbenzene)Rh][PF(6)](2), 6+, in a two-legged piano-stool geometry. The structural and electronic properties of this novel complex including a single-crystal EPR analysis are reported. The complex can be cleanly interconverted with its Rh(I) form, allowing for a comparison of the structural properties and reactivity of both oxidation states.