Reviewerly/exHarmony huggingface dataset:
The Spike Frequency Modulation (SFM) Theory for Neuroinformatics and Cognitive Cybernetics

One of the fundamental problems in neurology and neuroinformatics is whether the neural signals in human nerves systems are digital or analogue. This paper presents a novel neural signaling theory of Spike Frequency Modulation (SFM), which explains the nature of neural signals and their transformation in the nervous systems of the brain. Mathematical models of the unified signals of neural spikes across the sensory, associate and motor neurons are formally described. The time-divided mechanism for neural signal transmission and the space-divided mechanism for neural semantic representation in human nervous systems are rigorously explained. A set of experimental simulations demonstrates the SFM theory and the cognitive mechanisms of the neural pathways and networks. The SFM theory reveals the neurological and cognitive foundations of both natural and artificial neural networks for brain-inspired systems and engineering applications.
Formation of career strategies of young scholars in the area of computer sciences

The subject of this research is the career strategies of young scholars in the area of computer sciences. The object of this research is the educational space for formation of career strategies of young scholars in the area of computer sciences. The author examines the formation of IT educational space in Saint Petersburg. The goal consists in conducting a systemic analysis of educational space for the formation of career strategies of young scholars in the area of computer sciences. The research was carried out in Saint Petersburg in 2018, included 12 expert interviews with HR and IT specialists. The paper reviews such elements of educational space of information technologies as schools specialized in physics and mathematics, school and student Olympiads and contests, educational initiatives created by the leading IT companies of the city. The scientific novelty lies in introduction of educational IT space. At the first level it consists of the created back in Soviet time physical-mathematical schools and school Olympiads, which are also a step to higher education. The student environment has informal organizations: interest clubs, tournaments and contests. Namely in such educational space, young scholars in the area of computer technologies make their career choices.
PIV4DB: Probabilistic Integrity Verification for Cloud Database

Many organizations and enterprises use cloud databases to store data to improve management efficiency and save costs. However, cloud service providers may hide the fact that data integrity has been compromised for protecting their business reputation. Thus, how to verify the data integrity of cloud database in an effective way is very important for data owner. Existing integrity verification methods usually require cloud service provider to develop additional interfaces which are hard to be actually deployed. In addition, they cannot effectively detect tampering and deletion of a small amount of data. This paper presents a novel probabilistic integrity verification scheme (called PIV4DB) to address above challenges. Different from traditional methods, PIV4DB efficiently verifies the data integrity of cloud database by randomly selecting part of groups of tuples instead of querying all the tuples. Experimental results demonstrated that with validating 0.5% among 100k groups, PIV4DB could detect the corruption with 99% probability when the integrity of 920 out of billions of tuples are compromised. In addition, PIV4DB does not need extra cooperation with cloud service provider by just adding a new column of random numbers to the database and only using standard SQL statements to verify integrity.
Low-Resolution Face Recognition Method Combining Super-Resolution and Improved DCR Model

Low-resolution face recognition is a challenging problem in the face recognition algorithm. Most face recognition algorithms have a significant reduction in recognition accuracy when facing low-resolution faces. In this paper, we propose a DCR(Deep Coupled ResNet) model based on super-resolution reconstruction, named SR-DCR model, to tackle this challenging. Firstly, we designed a super-resolution face reconstruction model based on the facial feature. The "perceptual loss + classification loss" is used as the loss function of the reconstructed network. And the facial feature is used to constrain the reconstruction process to make the reconstructed face more favorable for recognition. Then a series of improvements and adjustments were made to the DCR model. Improved the network structure of the main network, and its cross-layer connection method was changed to a close connection, which further promoted feature propagation in the network and enhanced feature reusability; The loss function of the main network is improved, and the improved "triplet loss" is used as a loss function to directly optimize the facial feature between high resolution and low-resolution, further enhancing the high and low resolution. The aggregation between facial features; at the same time, the training method of the DCR model is adjusted, and the steps of joint training between the main network and branch network are added to further optimize DCR network parameters. Finally, the super-resolution reconstruction and the improved DCR model are combined to recognize low-resolution faces. The performance of the proposed algorithm is compared with that of other advanced methods on LFW and SCFace datasets. Experimental results show that the algorithm improves the recognition rate by 3.5% based on DCR. Effectively improve the accuracy of face recognition at low resolution.
Structured Visual Search via Composition-aware Learning

This paper studies visual search using structured queries. The structure is in the form of a 2D composition that encodes the position and the category of the objects. The transformation of the position and the category of the objects leads to a continuous-valued relationship between visual compositions, which carries highly beneficial information, although not leveraged by previous techniques. To that end, in this work, our goal is to leverage these continuous relationships by using the notion of symmetry in equivariance. Our model output is trained to change symmetrically with respect to the input transformations, leading to a sensitive feature space. Doing so leads to a highly efficient search technique, as our approach learns from fewer data using a smaller feature space. Experiments on two large-scale benchmarks of MS-COCO and HICO-DET demonstrates that our approach leads to a considerable gain in the performance against competing techniques.
A simple proof of QBF hardness

We provide a simple and direct proof of the exponential hardness of the KBKF formulas in the proof system Q-Resolution.
Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset

Hypertension is a serious medical condition that significantly increases the risk of chronic diseases. Early detection of individuals at risk for hypertension allows to prevent and delay the incidence of related diseases and strokes. In recent years, numerous researches have been focused on the decision support system for predicting hypertension. However, the class imbalance has commonly occurred problem in real-world applications. In this paper, we present the end-to-end cost-sensitive neural network (COST-NN) framework incorporated with a weighted random forest-based feature selection technique to predict hypertension among Korean adults. First, it identifies the best representative features using a weighted random forest-based feature selection technique. Then, we apply the COST-NN for predicting target among hypertensive and non-hypertensive individuals. In order to identify the accurate predictive model, we compare the various baseline models. Experimental results showed that COST-NN outperforms the regular state-of-the-art baseline models. In addition, the presented framework is expected to apply not only hypertension but also can support to prevent the patients from the risk of various diseases.
Critical Moments: Supporting Homeless Journeys Through Design

Individuals facing homelessness navigate through different moments and situations that bring specific challenges to their lives. A thorough investigation of their ‘journeys’ through such a difficult experience can provide new opportunities about how technologies can support individuals through homelessness. Based on a qualitative interview study involving nine individuals struggling with homelessness in an Australian city, we aimed to understand and trace critical moments of their struggles through a ‘journey tracking’ exercise. Our findings unpacked challenges and everyday resilient practices surrounding homelessness and the role technology can play in it. We discuss implications around developing services to provide emergency support to the homeless population based on our findings.
Facebook: Regulating Hate Speech in the Asia Pacific

This study was funded by the Facebook Content Policy Research on Social Media Awards. It maps hate speech law in India, Myanmar, Indonesia, The Philippines and Australia. It compares a definition of hate speech from scholarlship with Facebook’s. It explores how FB policies and procedures moderate content, by examining corporate literature, conducting interviews with staff, and mapping organisational responses. It analyses hate speech from the public FB pages of LGBTQ+ groups that escaped filters. It recommends FB: • extend consultation with groups • develop and publicise its trusted partners channel for crisis reporting . • hold an annual stakeholder regional roundtable • support administrators as critical gatekeepers with training and education.
Policing Fraud in Two Police Jurisdictions: England and Wales and South Korea

Cyber fraud is a major aspect of the crime trend in modern society, and there has been a significant increase in its scale as well as impact. The patterns of cyber fraud vary widely from simple crimes to highly specialized intelligent crimes, and are closely related to the characteristics of cyberspace. As online communication and interactions expand, new types of cyber fraud continue to emerge, and its modus operandi is also expected to become more intelligent with constantly updated information technology, as seen in cryptocurrency-related illegal activities. It is almost impossible to comprehend the whole picture of fraud varieties (Yar and Steinmetz, 2019). Crimes that occur in cyberspace are generally more complex because of the nature of cyberspace. Likewise, fraud offences in cyberspace do not coincide with the place of action, and the offenders’ identities are undisclosed. Predicated on these factors, cyber fraud spreads rapidly and widely (...
On lattice-free boosted MMI training of HMM and CTC-based full-context  ASR models

Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria. However, they have vastly different legacies and are usually implemented in different frameworks. In this paper, by decoupling the concepts of modeling units and label topologies and building proper numerator/denominator graphs accordingly, we establish a generalized framework for hybrid acoustic modeling (AM). In this framework, we show that LF-MMI is a powerful training criterion applicable to both limited-context and full-context models, for wordpiece/mono-char/bi-char/chenone units, with both HMM/CTC topologies. From this framework, we propose three novel training schemes: chenone(ch)/wordpiece(wp)-CTC-bMMI, and wordpiece(wp)-HMM-bMMI with different advantages in training performance, decoding efficiency and decoding time-stamp accuracy. The advantages of different training schemes are evaluated comprehensively on Librispeech, and wp-CTC-bMMI and ch-CTC-bMMI are evaluated on two real world ASR tasks to show their effectiveness. Besides, we also show bi-char(bc) HMM-MMI models can serve as better alignment models than traditional non-neural GMM-HMMs.
Reinforcement Learning for Education: Opportunities and Challenges.

This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a community-building effort to bring together researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). This article aims to provide an overview of the workshop activities and summarize the main research directions in the area of RL for ED.
The Variance of Variance

Chance models—mechanisms that explain empirical regularities through unsystematic variance—have a long tradition in the sciences but have been historically marginalized in management scholarship. An exception is the work of James G. March and his coauthors, who proposed a variety of chance models that explain important management phenomena, including the careers of top executives, managerial risk taking, and organizational anarchy, learning, and adaptation. This paper serves as a tribute to the beauty of these “little ideas” and demonstrates how they can be recombined to generate novel implications. In particular, we focus on the example of an inverted V-shaped performance association among the executives featured in one of the most prominent lists of executives, Barron’s annual list of Top 30 chief executive officers. A reanalysis of March and Shapira’s 1992 model provides a novel explanation for why many of the executives’ exceptional performances did not persist. In contrast to the usual explanations of complacency, hubris, and statistical regression, the results show that declines may result from these executives’ slow adaptation, incompetence, and self-reinforced risk taking. We conclude by elaborating on the normative implications of Jim’s chance models, which address many modern management and societal challenges. We further encourage the continued development of chance models to help explain performance differences, shifting from accounts that favor heroic stories of corporate leaders toward accounts that favor those leaders’ changing fortunes.
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual,  Interactive, and Ecological Environments

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation. These activities are designed to be realistic, diverse, and complex, aiming to reproduce the challenges that agents must face in the real world. Building such a benchmark poses three fundamental difficulties for each activity: definition (it can differ by time, place, or person), instantiation in a simulator, and evaluation. BEHAVIOR addresses these with three innovations. First, we propose an object-centric, predicate logic-based description language for expressing an activity's initial and goal conditions, enabling generation of diverse instances for any activity. Second, we identify the simulator-agnostic features required by an underlying environment to support BEHAVIOR, and demonstrate its realization in one such simulator. Third, we introduce a set of metrics to measure task progress and efficiency, absolute and relative to human demonstrators. We include 500 human demonstrations in virtual reality (VR) to serve as the human ground truth. Our experiments demonstrate that even state of the art embodied AI solutions struggle with the level of realism, diversity, and complexity imposed by the activities in our benchmark. We make BEHAVIOR publicly available at behavior.stanford.edu to facilitate and calibrate the development of new embodied AI solutions.
Cyber-Attack Detection from IoT Benchmark Considered as Data Streams

With the advancement of internet technologies, network traffic monitoring and cyber-attack detection are becoming more and more important for critical infrastructure. Unfortunately, there are still relatively few works in the literature that interpret the available benchmark data as data streams and take into account the dynamic characteristics of network packet tracking. The following work proposes an approach to generating data streams from the IoT-23 dataset and evaluates the resulting streams based on their suitability for use in network intrusion detection research.
Detecting indicators of compromise in web applications using access logs

There are many web applications that have security issues and reports from the largest security companies indicate that with the passage online due to the pandemic, web applications have increased in number and attackers have begun to attack them more and more. There are even reports that web applications have come first as the preferred method for attackers to enter a network. The most attacked web applications are those whose source code is public, and then anyone can see the source code to obtain unauthorized access to the application. In this article we want to look at things from another angle. We want to focus more on unauthorized access and not on the vulnerability through which someone obtained that unauthorized access. We propose an automatic method by which we determine at the level of access log those lines which indicates unauthorized access. One of the biggest advantages that this method, we propose, brings to the security of web applications, is that it does not focus on the complexity of the attack, but more if the attack exists or not. And one of the disadvantages of this method would be that we cannot find out through which vulnerability an unauthorized access was reached. In our tests we noticed that for some applications, depending on how they are built and how they respond to user requests, we can use with great success our method by which we discover these unauthorized accesses.
Visually Monitoring the Performance of a Component-Based Robot

The ever-increasing complexity of robots usually implies a parallel increase in the number of failures of such systems. Due to this, monitoring and anomaly detection plays a key role in the implementation of smart robotics and soft computing can significantly contribute to this task. In keeping with this idea, recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs) are proposed in present paper to monitor the performance and improve anomaly detection in a component-based robotic software. Furthermore, the original HUEP formulation is extended by means of density-based clustering. Such clustering techniques are validated in conjunction with unsupervised exploratory projection ones. This novel proposal is validated on an open and up-to-date dataset containing information about the software performance of a collaborative robot.
Combining Event Calculus and Description Logic Reasoning via Logic Programming

The paper introduces a knowledge representation language that combines the event calculus with description logic in a logic programming framework. The purpose is to provide the user with an expressive language for modelling and analysing systems that evolve over time. The approach is exemplified with the logic programming language as implemented in the Fusemate system. The paper extends Fusemate’s rule language with a weakly DL-safe interface to the description logic \(\mathcal ALCIF\) and adapts the event calculus to this extended language. This way, time-stamped ABoxes can be manipulated as fluents in the event calculus. All that is done in the frame of Fusemate’s concept of stratification by time. The paper provides conditions for soundness and completeness where appropriate. Using an elaborated example it demonstrates the interplay of the event calculus, description logic and logic programming rules for computing possible models as plausible explanations of the current state of the modelled system.
NBA Basketball Video Summarization for News Report via Hierarchical-Grained Deep Reinforcement Learning

At present, the demand for short video generation is increasing, especially for sports news report, which urgently needs automatic video summarization methods to reduce time and labor cost. This paper focuses on NBA basketball videos and seeks for the actual needs of news report on sports video summarization. We propose a hierarchical-grained deep reinforcement learning framework to generate short basketball video. For a long basketball game video, we propose a hierarchical-grained subshot segmentation algorithm, which takes into account both semantics and objective factors, and preserves spatiotemporal consistency. Then we select candidate frames through a news element enhanced deep reinforcement learning framework. On this basis, a news report oriented video summarization algorithm based on probability sampling is implemented with the fusion of multi-game and multi-news elements. Experimental results on the NBA dataset newly collected by us demonstrate the effectiveness of the proposed framework. Moreover, the proposed method is able to highlight the video content including well preserved news elements.
Multiple Scale Aggregation with Patch Multiplexing for High-Speed Inter-Vehicle Distance Estimation

We propose an accurate and robust inter-vehicle distance estimation method using highspeed stereo vision. The framework involves two phases: a tracking phase, wherein a preceding vehicle is accurately and stably tracked by a tracking algorithm optimized for stereo high-speed vision, and a distance estimation phase, wherein the inter-vehicle distance is estimated via a highly accurate scale estimation and aggregation method for multiple scale-based distance estimations to ensure that it is more accurate and robust without introducing a delay. Further, we propose patch multiplexing to realize accurate and efficient aggregation even in situations where the scale changes rapidly (e.g., emergency braking). Through comparative analysis using three real-world scenarios, we verify that the accuracy of inter-vehicle distance estimation using our approach is comparable to that of laser rangefinders. We also demonstrate that differential quantities, such as velocity and acceleration, could be accurately estimated using an adaptive Kalman filter. Our results will help develop safe and accurate truck platooning and adaptive cruise control systems.
Machine Interpretation of Medical Images Using Deep Learning

We come across different biomedical images. It is difficult to interpret those images as they do not have any description. Image captioning is the process of generating textual description from an image which depends on the object and action in the image. With the advancement in deep learning techniques, we will build models to generate captions for biomedical images. This model will be very useful to accelerate the diagnosis process by telling the abnormalities present in the image. The model will be based on an encoder-decoder framework along with an attention model. The encoder will be using deep CNN to extract image features and the decoder will be using transformers to generate captions. Caption generating involves different complex scenarios starting from collecting the data set, training the model, validating the model, creating trained model to test the image, detecting the image and generating the captions