Reviewerly/exHarmony huggingface dataset:
Educational Technology Adaptation &amp; Implication for Media Technology Adoption in the Period of COVID-19

Following the COVID-19 pandemic outbreak, many institutions immediately adapted multimedia electronic learning technologies, to provide enablement of electronic learning system, shifting from in-person classroom attendance to online synchronous and asynchronous transmissions. In the current paper, the goal of multimedia electronic learning system is reviewed through the combination of various pedagogical media tools that enabled wide range of curricula presentation. This study has considered four hundred postgraduate scholars from the faculty of computer science and information technology that adopted multimedia electronic learning systems to guarantee that the scheduled graduation date was not surpassed on the account of the institution lockdown. In this paper, the integrated system of creating personalized and self-directed learning through multimedia pedagogical methodology has been highlighted. The study sought to draw attention to the importance of creating an immersive and interactive learning environment using AI-mediated innovation, which provide students with the increased skills required to become cognizant and reflective digital natives. The paper has portrayed that an increased level of electronic engagement via interactive media tools, link to the requirements for the innovative educational transformation which demand an execution of educational curriculum. The educational goals and needs are first defined, and then the most effective learning environment for students has been designed.
On Some Applications of the Orthogonal Systems of Functions

When developing automatic signal processing systems, it is important to determine a set of parameters in order to optimize them and increase their efficiency. In the paper the modified Fourier transformation methods for digital signal processing have been used. A new approach to the construction of special polynomials has been offered by means of which the summing function is realized. The matrix methods of summation with rectangular matrices and their efficiency in comparison with the methods based on the use of triangular matrices have been investigated. Necessary and sufficient conditions for uniform convergence of the constructed trigonometric series for any continuous function on a segment in terms of a system of numerical coefficients have been established. The Lebesgue convergence of the constructed series for any summable periodic function has been studied. The obtained results can be used in digital signal processing systems based on the use of Fourier transformations.
Plastaumatic: Automating plastome assembly and annotation

Plastome sequence data is most often extracted from plant whole genome sequencing data and need to be assembled and annotated separately from the nuclear genome sequence. In projects comprising multiple genomes, it is labour intense to individually process the plastomes as it requires many steps and software. This study developed Plastaumatic - an automated pipeline for both assembly and annotation of plastomes, with the scope of the researcher being able to load whole genome sequence data with minimal manual input, and therefore a faster runtime. The main structure of the current automated pipeline includes trimming of adaptor and low-quality sequences using fastp , de novo plastome assembly using NOVOPlasty , standardization and quality checking of the assembled genomes through a custom script utilizing BLAST+ and SAMtools , annotation of the assembled genomes using AnnoPlast , and finally generating the required files for NCBI GenBank submissions. The pipeline is demonstrated with 12 potato accessions and three soybean accessions.
Differential Fault Attack on PHOTON-Beetle

In this paper, we report the first differential fault attack (DFA) on nonce-based AE scheme PHOTON-BEETLE, which is one of the finalists in the ongoing NIST LwC competition. In general, it is a challenging task to perform DFA for any nonce-based sponge AE because of a unique nonce in the encryption query. However, the decryption procedure (with a fixed nonce) is still susceptible to DFA. We propose two fault attack models, and for both, we give theoretical estimates of the number of faulty queries to get multiple forgeries. Our simulated values corroborate closely the theoretical estimates. Finally, we devise an algorithm to recover the state based on the collected forgeries. Under the random fault attack model, to retrieve the secret key, we need approximately 2^37.15 number of faulty queries. Also, the offline time and memory complexities of this attack are respectively 216 and 210 nibbles. In the known fault attack model, we need around 211.05 number of faulty queries to retrieve the secret key. Also, the time and memory complexities of this state recovery attack are respectively 211 and 29 nibbles. Further, we have reduced the number of faulty queries to 640 under the precise bit-flip fault model.
Group anomaly detection for spatio-temporal collective behaviour scenarios in smart cities

Group anomaly detection in terms of detecting and predicting abnormal behaviour from entities as a group rather than as an individual, addresses a variety of challenges in spatio-temporal environments like e.g. traffic and transportation systems, smart cities, geoinformation systems, etc. They provide information about a commonly large number of individual entities. Examples for such entities would be airplanes and drones, vehicles, ships but also people, remote sensors and any other information source in interaction with the environment. However, as point anomaly detection is quite common for revealing the abnormal behaviour of individual entities, the collective behaviour of the individuals as a group remains completely uncovered. For example potential for traffic flow optimizations or increased local traffic guideline violations cannot be detected by one single drive but by considering the behavior of a group of vehicle drives in this area. With this work-in-progress we elaborate the potential of group anomaly detection algorithms for spatio-temporal collective behaviour scenarios in smart cities. We describe the group anomaly detection problem in the context of urban planning and demonstrate its effectiveness on a public real-world data set for urban rental bike rides and stations in and around Munich revealing abnormal groups of rides, which allows to optimize the rental bike accessibility to the population and with that to contribute to a sustainable environment.
Histogram-Based Transformation Function Estimation for Low-Light Image Enhancement

We propose a learning-based low-light image enhancement algorithm, called the histogram-based transformation function estimation network (HTFNet), that estimates transformation functions using the histogram of an input image. First, we obtain an attention image that indicates the pixel-wise information on the level of enhancement. Then, the proposed HTFNet generates the transformation functions by exploiting both the spatial and statistical information of the input image by combining two feature maps extracted from the input image and its histogram. Finally, the enhanced images are obtained via channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality compared with the state-of-the-art algorithms.
Forward Error Correction Applied to JPEG-XS Codestreams

JPEG-XS offers low complexity image compression for applications with constrained but reasonable bit-rate, and low latency. Our paper explores the deployment of JPEG-XS on lossy packet networks. To preserve low latency, Forward Error Correction (FEC) is envisioned as the protection mechanism of interest. Although the JPEG-XS codestream is not scalable in essence, we observe that the loss of a codestream fraction impacts the decoded image quality differently, depending on whether this codestream fraction corresponds to codestream headers, to coefficient significance information, or to low/high frequency data. Hence, we propose a rate-distortion optimal unequal error protection scheme that adapts the redundancy level of Reed-Solomon codes according to the rate of channel losses and the type of information protected by the code. Our experiments demonstrate that, at 5% loss rates, it reduces the Mean Squared Error by up to 92% and 65%, compared to a transmission without and with optimal but equal protection, respectively.
Enforcing Fine-grained Constant-time Policies

Cryptographic constant-time (CT) is a popular programming discipline used by cryptographic libraries to protect themselves against timing attacks. The CT discipline aims to enforce that program execution does not leak secrets, where leakage is defined by a formal leakage model. In practice, different leakage models coexist, sometimes even within a single library, both to reflect different architectures and to accommodate different security-efficiency trade-offs.
Enhancing Sentiment Analysis via Random Majority Under-Sampling with Reduced Time Complexity for Classifying Tweet Reviews

Twitter has become a unique platform for social interaction from people all around the world, leading to an extensive amount of knowledge that can be used for various reasons. People share and spread their own ideologies and point of views on unique topics leading to the production of a lot of content. Sentiment analysis is of extreme importance to various businesses as it can directly impact their important decisions. Several challenges related to the research subject of sentiment analysis includes issues such as imbalanced dataset, lexical uniqueness, and processing time complexity. Most machine learning models are sequential: they need a considerable amount of time to complete execution. Therefore, we propose a model sentiment analysis specifically designed for imbalanced datasets that can reduce the time complexity of the task by using various text sequenced preprocessing techniques combined with random majority under-sampling. Our proposed model provides competitive results to other models while simultaneously reducing the time complexity for sentiment analysis. The results obtained after the experimentation corroborate that our model provides great results producing the accuracy of 86.5% and F1 score of 0.874 through XGB.
Two-Stream Based Multi-Stage Hybrid Decoder for Self-Supervised Multi-Frame Monocular Depth

Self-supervised depth estimation has attracted a lot of attention recently due to its low cost. Despite using the self-supervision from image sequences, the current single-image based methods only infer depth from the scene information ignoring the matching information which is also important. Nevertheless, the matching information is not always reliable, especially in the texture-less and occlusion regions. Thus it would be attractive to combine the strength of single-image scene information and multi-frame matching information. In this letter, we propose a two-stream based multi-stage hybrid decoder to effectively accomplish the integration procedure. The hybrid decoder consists of two pathways for these two kinds of information respectively, and interactively fuses them. Specifically, a cost volume is built based on the scene prior to represent the matching information, and feeds back to the single-image pathway to complete the integration. To further facilitate the interactive integration, a multi-stage fusion strategy is embedded seamlessly into the hybrid decoder, resulting in more accurate depth results. Our approach outperforms the existing self-supervised methods on the KITTI and Cityscapes datasets.
Scalable Networks of Neuromorphic Photonic Integrated Circuits

Neuromorphic photonic integrated circuits over silicon photonic platform have recently made significant progress. Photonic neural networks with a small number of neurons have demonstrated important applications in high-bandwidth, low latency machine learning (ML) type signal processing applications. Naturally an important topic is to investigate building a large scale photonic neural networks with high flexibility and scalability to potentially support ML type applications involving high-speed processing of a high volume of data. In this paper we revisited the architecture of microring resonator (MRR) -based non-spiking and spiking photonic neurons, and photonic neural networks using broadcast-and-weight scheme. We illustrate expanded neural network topologies by cascading photonic broadcast loops, to achieve scalable neural network scalability with a fixed number of wavelengths. Furthermore, we propose the adoption of wavelength selective switch (WSS) inside the broadcasting loop for wavelength-switched photonic neural network (WS-PNN). The WS-PNN architecture will find new applications of using off-chip WSS switches to interconnect groups of photonic neurons. The interconnection of WS-PNN can achieve unprecedented scalability of photonic neural networks while supporting a versatile selection of mixture of feedforward and recurrent neural network topologies.
CETIS

Intel control-flow enforcement technology (CET) is a new hardware feature available in recent Intel processors. It supports the coarse-grained control-flow integrity for software to defeat memory corruption attacks. In this paper, we retrofit CET, particularly the write-protected shadow pages of CET used for implementing shadow stacks, to develop a generic and efficient intra-process memory isolation mechanism, dubbed CETIS.
From Self-Tracking to Sleep-Hacking

With growing interest in how technology can make sense of our body and bodily experiences, this work looks at how these experiences are communicated through and with the help of technology. We present the ways in which knowledge about sleep, and how to manipulate it, is collectively shared online. This paper documents the sleep-change practices of four groups of 'Sleep Hackers' including Nurses, Polyphasic Sleeper, Over-sleepers, and Biohackers. Our thematic analysis uses 1002 posts taken from public forums discussing sleep change. This work reveals the different ways individuals share their experiences and build communal knowledge on how to 'hack' their sleep -- from using drugs, external stimulation, isolation, and polyphasic sleeping practices where segmented sleep schedules are shared between peers. We describe how communal discussions around the body and sleep can inform the development of body sensing technology. We discuss the opportunities and implications for designing for bodily agency over sleep changes both in relation to collaboratively developed understandings of the body and social context of the user. We also discuss notions of slowly changing bodily processes and sensory manipulation in relation to how they can build on the exploration of soma-technology.
Holding on to Compliance While Adopting DevSecOps: An SLR

The software industry has witnessed a growing interest in DevSecOps due to the premises of integrating security in the software development lifecycle. However, security compliance cannot be disregarded, given the importance of adherence to regulations, laws, industry standards, and frameworks. This study aims to provide an overview of compliance aspects in the context of DevSecOps and explore how compliance is ensured. Furthermore, this study reveals the trends of compliance according to the extant literature and identifies potential directions for further research in this context. Therefore, we carried out a systematic literature review on the integration of compliance aspects in DevSecOps, which rigorously followed the guidelines proposed by Kitchenham and Charters. We found 934 articles related to the topic by searching five bibliographic databases (163) and Google Scholar (771). Through a rigorous selection process, we selected 15 papers as primary studies. Then, we identified the compliance aspects of DevSecOps and grouped them into three main categories: compliance initiation, compliance management, and compliance technicalities. We observed a low number of studies; therefore, we encourage further efforts into the exploration of compliance aspects, their automated integration, and the development of metrics to evaluate such a process in the context of DevSecOps.
Making a Radical Misogynist

The algorithms and the interactions facilitated by online platforms have been used by radical groups to recruit vulnerable individuals to their cause. This has resulted in the sharp growth of violent events and deteriorating online discourse. The Manosphere, a collection of radical anti-feminist communities, is one such group that has attracted attention due to its rapid growth and increasingly violent real-world outbursts. In this paper, we examine the social engagements between Reddit users who have participated in feminist discourse and the Manosphere communities on Reddit to understand the process of development of traits associated with the adoption of extremist ideologies. By using existing research on the psychology of radicalization we track how specific types of social engagement with the Manosphere influence the development of traits associated with radicalization. Our findings show that: (1) participation, even by the simple act of joining the Manosphere, has a significant influence on the language and outlook traits of a user, (2) Manosphere elites are extremely effective propagators of radical traits and cause their increase even outside the Manosphere, and (3) community perception can heavily influence a user's behavior. Finally, we examine how our findings can help draft community and platform moderation policies to help mitigate the problem of online radicalization.
Implementation of Improved Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot

An autonomous mobile robot (AMR) with wheel drive is one of the most widely developed applications today. Navigation ability, especially path planning, is one of the problems faced in AMR development. One of the path planning algorithms that is considered reliable and can be implemented in real-time is the artificial potential field (APF). However, the weakness of APF is that the robot can be trapped in the minimum locale. The local minimums commonly encountered are goal non-reachable due to nearby obstacles (GNRON) and symmetrically aligned robot obstacle goals (SAROG). This study aims to develop an APF-based path planning algorithm to solve the local minimum problem. The Gompertz function and the cone-shaped potential field are used in the Improved APF (IAPF) algorithm. The IAPF algorithm is also implemented in the kinematic equation of a wheeled mobile robot with a differential drive type. The results show that the IAPF algorithm can be implemented in a differential drive type robot. The robot can avoid obstacles in the form of SAROG and GNRON and go to the goal with an error to the goal $(d_{rg})$ less than the tolerance value of 5%.
An Experiment on Localization of Ontology Concepts in Deep Convolutional Neural Networks

Deep neural networks have recently evolved into a powerful AI tool, reaching near-human performance level in many tasks, and in some tasks even surpassing it. However, a significant drawback of neural networks is the lack of explainability and interpretability — it is hard to say why a neural network arrived to a certain conclusion. This significantly limits application of neural networks in critical tasks and undermines trust in human-AI collaboration. It has been recently shown that internal representations constructed by a neural network can often be aligned with a domain ontology. This opens a promising way to provide explanations of a neural network in human terms. In this paper, we discuss the results of the experiment aimed at understanding what layers of a neural network are the most perspective for the alignment with given ontology concept. To do so, we build concept localization maps for XTRAINS — a synthetic dataset consisting of images and their ontological annotations. The importance of such maps is that they can be used for the development of efficient concept alignment heuristics. The experiment mostly supports the intuition that high-level concepts are localized mostly in the activations of last layers of a neural network (near its head), while lower-level concepts might be better extracted from middle layers.
Learning From the Future: Light Cone Modeling for Sequential Recommendation

Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user’s present interests from his/her past-to-present behaviors, i.e., making recommendations with only the unidirectional past information. This article argues that future information is another critical factor for the sequential recommendation. However, directly learning from future-to-present behaviors inevitably causes data leakage. Here, it is pointed out that future information can be learned from users’ collaborative behaviors. Toward this end, this article introduces sequential graphs to depict item transition relationships: where and how each item transits from and will transit to. This temporal evolution information is called the light cone in special and general relativity. Then, a bidirectional sequential graph convolutional network (BiSGCN) is proposed to learn item representations by encoding past and future light cones. Finally, a manifold translating embedding (MTE) method is proposed to model item transition patterns in Riemannian manifolds, which helps to better capture the geometric structures of light cones and item transition patterns. Experimental comparisons and ablation studies verify the outstanding performance of BiSGCN, the benefits of learning from the future, and the improvements of learning in Riemannian manifolds.
Multi-Model Inference Accelerator for Binary Convolutional Neural Networks

Binary convolutional neural networks (BCNN) have shown good accuracy for small to medium neural network models. Their extreme quantization of weights and activations reduces off-chip data transfer and greatly reduces the computational complexity of convolutions. Further reduction in the complexity of a BCNN model for fast execution can be achieved with model size reduction at the cost of network accuracy. In this paper, a multi-model inference technique is proposed to reduce the execution time of the binarized inference process without accuracy reduction. The technique considers a cascade of neural network models with different computation/accuracy ratios. A parameterizable binarized neural network with different trade-offs between complexity and accuracy is used to obtain multiple network models. We also propose a hardware accelerator to run multi-model inference throughput in embedded systems. The multi-model inference accelerator is demonstrated on low-density Zynq-7010 and Zynq-7020 FPGA devices, classifying images from the CIFAR-10 dataset. The proposed accelerator improves the frame rate per number of LUTs by 7.2× those of previous solutions on a ZYNQ7020 FPGA with similar accuracy. This shows the effectiveness of the multi-model inference technique and the efficiency of the proposed hardware accelerator.