Reviewerly/exHarmony huggingface dataset:
Real-Time Video Anomaly Detection for Smart Surveillance

Human monitoring of surveillance cameras for anomaly detection may be exhaustive and monotonous task as it requires constant attention to judge if the captured activities are anomalous or suspicious. Hence, this paper presents a real-time expert system based on computer vision technology and deep learning models for autonomous video anomaly detection (AVAD). First, background subtraction (BS) was performed on each frame by modeling each pixel as a mixture of Gaussians (MoG) to concatenate only the higher order learning in the foreground. Next, deep learning techniques are customized to filter out unnormal events from normal ones and automatically identify signs of threat and violence in real-time. The vision-based system employs autoencoders for their ability to work with little to no supervision. Specifically, convolutional autoencoders are used as semisupervised learning of convolution filters to minimize reconstruction errors by learning the optimal filters. Then, object detection was introduced to minimize human intervention in video stream processing. At recognition time, the network generates an alarm for the presence of an anomaly to notify of the identification of a potentially dangerous scenario, showing in a time instance the suspicious actions. Finally, the complete system is validated upon several benchmark datasets and proven to be robust for complex video anomaly detection. The average Area Under the Curve (AUC) value of the frame-level evaluation for all benchmarks is 94.94%. The best improvement ratio of AUC between AVAD and state-of-the-art methods is 7.7%.
Forseti: Dynamic chunk-level reshaping for data processing on heterogeneous clusters

Data-intensive computing frameworks typically split job workload into fixed-size chunks, allowing them to be processed as parallel tasks on distributed machines. Ideally, when the machines are homogeneous and have identical speed, chunks of equal size would finish processing at the same time. However, such determinism in processing time cannot be guaranteed in practice. Diverging processing times can result from various sources such as system dynamics, machine heterogeneity, and variable network conditions. Such variation, together with dynamics and uncertainty during task processing, can lead to significant performance degradation at job level, due to long tails in job completion time resulted from residual chunk workload and stragglers. In this paper, we propose Forseti, a novel processing scheme that is able to reshape data chunk size on the fly with respect to heterogeneous machines and a dynamic execution environment. Forseti mitigates residual workload and stragglers to achieve significant improvement in performance. We note that Forseti is a fully online scheme and does not require any a priori knowledge of the machine configuration nor job statistics. Instead, it infers such information and adjusts data chunk sizes at runtime, making the solution robust even in environments with high volatility. In its implementation, Forseti also exploits a virtual machine reuse feature to avoid task start-up and initialization cost associated with launching new tasks. We prototype Forseti on a real-world cluster and evaluate its performance using several realistic benchmarks. The results show that Forseti outperforms a number of baselines, including default Hadoop by up to 68% and SkewTune by up to 50% in terms of average job completion time.
Jordan–Landau theorem for matrices over finite fields

Given a positive integer r and a prime power q, we estimate the probability that the characteristic polynomial fA(t) of a random matrix A in GLn(Fq) is square-free with r (monic) irreducible factors when n is large. We also estimate the analogous probability that fA(t) has r irreducible factors counting with multiplicity. In either case, the main term (log⁡n)r−1((r−1)!n)−1 and the error term O((log⁡n)r−2n−1), whose implied constant only depends on r but not on q nor n, coincide with the probability that a random permutation on n letters is a product of r disjoint cycles. The main ingredient of our proof is a recursion argument due to S. D. Cohen, which was previously used to estimate the probability that a random degree n monic polynomial in Fq[t] is square-free with r irreducible factors and the analogous probability that the polynomial has r irreducible factors counting with multiplicity. We obtain our result by carefully modifying Cohen's recursion argument in the matrix setting, using Reiner's theorem that counts the number of n×n matrices with a fixed characteristic polynomial over Fq.
AugPaste: One-Shot Anomaly Detection for Medical Images

Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially in the medical image analysis realm. In this work, we propose a one-shot anomaly detection framework, namely AugPaste, that utilizes true anomalies from a single annotated sample and synthesizes artificial anomalous samples for anomaly detection. First, a lesion bank is constructed by applying augmentation to randomly selected lesion patches. Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training. Finally, a classification network is trained using the synthetic abnormal samples and the true normal data. Extensive experiments are conducted on two publicly-available medical image datasets with different types of abnormalities. On both datasets, our proposed AugPaste largely outperforms several state-of-the-art unsupervised and semi-supervised anomaly detection methods, and is on a par with the fully-supervised counterpart. To note, AugPaste is even better than the fully-supervised method in detecting early-stage diabetic retinopathy.
Investigation of Training Multiple Instance Learning Networks with Instance Sampling

One challenge of training deep neural networks with gigapixel whole-slide images (WSIs) is the lack of annotation at pixel level or patch (instance) level due to the high cost and time-consuming labeling effort. Multiple instance learning (MIL) as a typical weakly supervised learning method aimed to resolve this challenge by using only the slide-level label without needing patch labels. Not all patches/instances are predictive of the outcome. The attention-based MIL method leverages this fact to enhance the performance by weighting the instances based on their contribution to predicting the outcome. A WSI typically contains hundreds of thousands of image patches. Training a deep neural network with thousands of image patches per slide is computationally expensive and requires a lot of time for convergence. One way to alleviate this issue is to sample a subset of instances/patches from the available instances within each bag for training. While the benefit of sampling strategies for decreasing computing time might be evident, there is a lack of effort to investigate their performances. This project proposes and compares an adaptive sampling strategy with other sampling strategies. Although all sampling strategies substantially reduce computation time, their performance is influenced by the number of selected instances. We show that if we are limited to only select a few instances (e.g., in order of 1 $$\sim $$ 10 instances), the adaptive sampling outperforms other sampling strategies. However, if we are allowed to select more instances (e.g., in order of 100 $$\sim $$ 1000 instances), the random sampling outperforms other sampling strategies.
Fundus Photograph Defect Repair Algorithm Based on Portable Camera Empty Shot

Fundus photograph is an important basis for ophthalmologists to diagnose retinal diseases. Due to the limitations of the optical system design for portable fundus cameras, there still exist typical image defects leading to low quality images. There are stray light defects such as atomization area, shadow ring, bright spot, central dark hole and so on. Since the camera empty shot in a dark environment can reflect important device-specific characteristics of typical defects, we propose a novel framework to execute image defects repairing by template compensation based on camera empty shots for portable fundus cameras. First, noise reduction is employed from a camera empty shot image. Then, a defect compensation template based on empty shot is generated. For each fundus image, an adjusted ratio is optimized in different defect areas of the customized compensation template. Finally, this template is applied to compensate and repair the stray light defects in order to improve image quality for the target image captured from the same camera. Experimental results show that our proposed method is effective, and it is able to obtain fundus images in better quality.
Game Design for Covertly Combating Covid-19 Vaccination Hesitancy

Digital games have over the past few years have become increasingly important, particularly during Covid-19 lockdowns, not just as a way to spend free time but also in many more serious fields. One such area is healthcare, where applied games are increasingly utilized to educate individuals and encourage compliance with health measures, such as vaccination. This paper analyses several techniques for developing persuasive games that help change people’s attitudes and therefore their behaviours, in this case focussed on Covid-19 vaccination. These techniques are based on a covert approach to persuasion to avoid triggering individuals’ reactance and hence reaching a wider audience. Evaluation of the designed game shows promising results in terms of the impact of emotional engagement on attitudes toward vaccine uptake.
On the Existence of Multiple RSA Private Keys

It is known that, given an RSA modulus, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> <mo>=</mo> <mi>p</mi> <mi>q</mi> </math> , the public key <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>e</mi> </math> and the corresponding private key <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>d</mi> </math> satisfy the modulo congruence <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>e</mi> <mi>d</mi> <mo>≡</mo> <mn>1</mn> <mfenced open="(" close=")" separators="|"> <mrow> <mi mathvariant="normal">mod</mi> <mi>ϕ</mi> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> </mrow> </mrow> </mfenced> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>ϕ</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> <mo>=</mo> <mfenced open="(" close=")" separators="|"> <mrow> <mi>p</mi> <mo>−</mo> <mn>1</mn> </mrow> </mfenced> <mfenced open="(" close=")" separators="|"> <mrow> <mi>q</mi> <mo>−</mo> <mn>1</mn> </mrow> </mfenced> </math> . Usually, the private key <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>d</mi> </math> can be computed efficiently using the extended Euclidean algorithm, and it is common knowledge that the private key is unique in the sense of modular <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>ϕ</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> </math> . This paper shows that there exist multiple private keys <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>d</mi> </math> ; they all satisfy that <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>d</mi> <mo>&lt;</mo> <mi>ϕ</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> </math> . This paper also presents the exact relationship between an RSA public key and a corresponding private key.
MODERATING EFFECT OF HUMAN RESOURCE POLICIES ON BALANCED CONTRACT AND PERFORMANCE OF ACADEMIC STAFF IN SELECTED PUBLIC UNIVERSITIES

Purpose: The objective of the study envisaged examining moderating effect of human resource policies on balanced contract and performance of academic staff in selected public universities.&#x0D; Methodology: This study was conducted using descriptive and explanatory research design. Equity theory, proposed by Adams (1963) and regarded as a justice theory, anchored this study. This study targeted six public universities whose student population are above 15,000 and a minimum of 4 similar schools across the universities. The target population was 6 selected public universities with a population size of 6,271 academic staff. The sample size was 362 academic staff of the selected schools, in the selected public universities. Primary data was collected using a questionnaire, bearing both structured and semi-structured questions. Given that the dependent variable in this study is continuous, multiple linear regression was performed in this investigation. The hypotheses was tested from the regression model output and presented by way of Υ=β0+ β1Х1 + β2Х2 + β3Х3 + ε.&#x0D; Findings: The study findings demonstrated that human resources policies significantly moderated the relationship between balanced contract and performance of academic staff in selected universities in Kenya. The study concluded that progressive and interactive human resource policies communicate the values the institutions attribute to staff and can cause either sustainable performance of staff or negatively impact on the type of contract between employees and institutions. These policies also affect directly, the performance of staff by determining their level of job satisfaction. Equity theory, asserts that employees attempt to preserve parity between their contributions to a job and the rewards they receive, as compared to others' perceived contributions and incentives.&#x0D; Unique Contribution to Theory, Practice and Policy: The study finally recommends that human resource departments streamline existing HR policies and formulate new policies that will enable recognition of existence of various psychological contracts and provide ways of ensuring employees adopt balanced contracts that will have positive impact on staff performance.
A Study on the Relationship between Journalism and Feminism in Britain

This paper aims to analyze the relationship between journalism and feminism in Britain and find out whether journalism became feminized over time by discussing how women engaged in the newsroom and the orientation of feminine feature in journalism in different stages. This essay adopted the historical approach and consisted of three parts. The first two parts reviewed the development of the female newspaper and broadcasting journalists in the initial stage and post-war period, and then the following paragraph discussed the overall situation of female journalists in contemporary British society. By analyzing the development of female journalists and their journalistic style in history, the trend of journalism would be revealed, as well as whether the influx of female journalists had made journalism became feminized over time in Britain.
High Speed Decoding for High-Rate and Short-Length Reed–Muller Code Using Auto-Decoder

In this paper, we show that applying a machine learning technique called auto-decoder (AD) to high-rate and short length Reed–Muller (RM) decoding enables it to achieve maximum likelihood decoding (MLD) performance and faster decoding speed than when fast Hadamard transform (FHT) is applied in additive white Gaussian noise (AWGN) channels. The decoding speed is approximately 1.8 times and 125 times faster than the FHT decoding for R(1,4) and R(2,4), respectively. The number of nodes in the hidden layer of AD is larger than that of the input layer, unlike the conventional auto-encoder (AE). Two ADs are combined in parallel and merged together, and then cascaded to one fully connected layer to improve the bit error rate (BER) performance of the code.
Using the K-associated Optimal Graph to Provide Counterfactual Explanations

Only recently have data mining results been thought to aid human interpretability. Explanations are useful to understand the reasons why (or why not) the model has (or hasn’t) achieved a given decision. Counterfactual explanations aim to explain why not the model yield an expected decision. A counterfactual explanation is usually done by a post-hoc algorithm which often requires access to training data or model details. Also, the majority of such algorithms do not generate robust explanations, once they assume the model is noise-free and will not be updated over time. This paper proposes a model-agnostic, training data-independent algorithm to provide robust counterfactual explanations. The proposed method generates data samples around the instance to be explained and builds a K-Associated Optimal Graph (KAOG) with those data. KAOG allows measuring how intertwined the data examples are regarding their classes. This way, the explanation method can search for an example that relies on a noise-free area in the attribute space, granting trust to the explanation. Experiment results on counterfactual feasibility and distance from query data show the effectiveness of the proposed algorithm when compared to ten state-of-the-art methods on three data sets.
Fuzzy Cognitive Maps Extraction from Enriched Tweets

Fuzzy Cognitive Maps (FCMs) represent graphically the main concepts of a given domain and their relationships as a directed and weighted graph. As part of a growing need for intelligent systems that produce explanations for the decisions they make (the so-called XAI – eXplainable Artificial Intelligence), due to their intuitive yet formal nature, FCMs are invaluable tools for modeling complex real world scenarios, but are traditionally created through the analysis of direct interviews with a number of domain experts, hence requiring a largely manual, expensive, and cumbersome effort. The aim of this work is to design, develop and test a method for the automatic generation of FCMs from raw data in form of Twitter conversations. In order to improve the recognized entities and to cope with brevity, ambiguity and jargon, messages in tweets are first enriched with both domain-specific and general corpora, then analyzed and transformed into meaningful maps. As the data come from a population of common users instead of domain experts, the obtained FCMs are highly variable and should be read more as a snapshot of the beliefs of these users on a specific topic than an objective representation of what experts think on that topic. From clerical review, reported test cases confirm the viability and effectiveness of the proposed method.
Impact of the Covid-19 pandemic on the requirement engineering process in small development projects: A case study

Requirement engineering (RE) is crucial for any software development project. It plays a vital role in the development lifecycle, as it lays the foundation for all subsequent development steps. Ensuring proper collection of project requirements makes the subsequent process, including design, development and testing, easier. The software industry has paid great attention to RE, including tools and techniques, starting with requirement elicitation. In the last couple of years, the Covid-19 pandemic has impacted processes, as meeting clients and stakeholders face-to-face and visiting them in their workplace has been made more difficult by social distancing, changing the techniques for requirement elicitation. Most software development companies rely on distant communication and alternative approaches to collect customers’ requirements to understand their needs, so they can reflect the reality in the work and try to persuade the customers that the developed solution meets their requirements. This study presents a case study of three educational projects at three different universities with different development requirements. The study compares the process before and during the pandemic to identify the challenges and features of the shift which occurred and measure its impact on number of requirements completed in both periods, as well as its impact on development project resources. This study's findings indicate increased requirements during the elicitation process and changes in human resources because of eliminating the practitioners over distance communication during the Covid-19 pandemic.
Technological model based on the Internet of Things (IOT) with QR code influence to reduce 2D printing times at the Continental University

The purpose of this study focuses on the development of a technology module based on IOT and QR technology, which can comprise thousands or hundreds of pages printed daily. First, we identified the strategy and objectives of our module for a deployment environment, from a consumer perspective in relation to the print service based on emerging technologies. Next, we established the resources used in the development of our module through a structured matrix for the proper management of resources. Then, we collected quantitative data on how long it would take to perform a usual printing (without the module implementation) and to perform the printing with implemented technological module. Data collection and methodology were carried out at Universidad Continental facilities in Huancayo city. The results obtained lead us to affirm that there is a significant difference in the mean printing times between technological module and the ordinary one. With this, we can conclude that use of technological module really had a significant effect with respect to time, considering also that this module is based on emerging technologies as highly permissible projects in today's market.
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems

Deep Reinforcement Learning uses a deep neural network to encode a policy, which achieves very good performance in a wide range of applications but is widely regarded as a black box model. A more interpretable alternative to deep networks is given by neuro-fuzzy controllers. Unfortunately, neuro-fuzzy controllers often need a large number of rules to solve relatively simple tasks, making them difficult to interpret. In this work, we present an algorithm to distill the policy from a deep Q-network into a compact neuro-fuzzy controller. This allows us to train compact neuro-fuzzy controllers through distillation to solve tasks that they are unable to solve directly, combining the flexibility of deep reinforcement learning and the interpretability of compact rule bases. We demonstrate the algorithm on three well-known environments from OpenAI Gym, where we nearly match the performance of a DQN agent using only 2 to 6 fuzzy rules.
P-IT2IFCM: Probabilistic Interval Type-2 Intuitionistic Fuzzy c-Means Clustering Algorithm

The recently introduced ‘Improved Probabilistic Intuitionistic Fuzzy c-Means algorithm (IPIFCM)’ provides a Probabilistic Euclidean Distance Measure (PEDM) based computationally efficient clustering technique. Since IPIFCM is defined on Type-1 Atanassov Intuitionistic Fuzzy Sets (AIFS), it is unable to capture the uncertainty of the membership and non-membership values of a given datapoint induced by the hesitancy factor of the AIFS. Interval Type-2 Fuzzy Sets (IT2 FSs) deals with the uncertainty in the membership values. In this paper, we incorporate IT2 FSs in the IPIFCM algorithm by introducing upper bound and lower bound of the membership (and non-membership) values of each datapoint to model the change caused by the hesitancy factor. Accordingly, this paper proposes the ‘Probabilistic Interval Type-2 Intuitionistic clustering algorithm’ (P-IT2IFCM), which uses the interval probabilistic weights for PEDM to propose ‘Interval Type-2 Probabilistic Euclidean Distance Measure’ (IT2PEDM). The proposed algorithm provides superior results to existing Fuzzy c-Means (FCM) algorithms such as the basic FCM algorithm, IFCM algorithm, Kernelized-IFCM algorithm and IPIFCM algorithm when executed over various benchmark UCI datasets.