Reviewerly/exHarmony huggingface dataset:
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation

Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to deploy them on resource-constrained edge devices. Though extensive efficient accelerator designs, from traditional electronics to emerging photonics, have been successfully demonstrated, they are still bottlenecked by expensive memory accesses due to tremendous gaps between the bandwidth/power/latency of electrical memory and computing cores. Previous solutions fail to fully-leverage the ultra-fast computational speed of emerging DNN accelerators to break through the critical memory bound. In this work, we propose a general and unified framework to trade expensive memory transactions with ultra-fast on-chip computations, directly translating to performance improvement. We are the first to jointly explore the intrinsic correlations and bit-level redundancy within DNN kernels and propose a multi-level in situ generation mechanism with mixed-precision bases to achieve on-the-fly recovery of high-resolution parameters with minimum hardware overhead. Extensive experiments demonstrate that our proposed joint method can boost the memory efficiency by 10-20× with comparable accuracy over four state-of-the-art designs, when benchmarked on ResNet-18/DenseNet-121/MobileNetV2/V3 with various tasks.
InterCriteria Analysis of the Blood Group Distribution of Patients of Saint Anna Hospital in 2015–2019

This paper presents findings from applying the method of InterCriteria Analysis to a dataset of 33782 patients of Saint Anna Hospital, Sofia whose blood was tested in the period 2015–2019. The dataset, after performed cleansing and anonymization, contains data of the blood groups of the tested patients, their birth year and sex and the research aims to detect dependences and changes over time of the distribution of the different blood types over the Bulgarian population, represented in this sample. The results of this study can potentially be informative also for the blood transfusion specialists in the country regarding the blood transfusion capacity of the population.
Fortis-ámyna-smart contract model for cross border financial transactions

A Futuristic Operating transactions Reaching beyond Territorial borders In blockchain Smart contract model (Fortis-ámyna) for cross border transactions in a quantum setting is proposed. A simple hybrid classical-quantum model has been described whose main ingredients are a classical blockchain capable of handling stateful smart contracts with quantum defence. This hybrid payment model uses quantum states as bank transactions and a classical blockchain to settle disputes and keep track of the valid reference numbers. The proposed model fortis ámyna has several desirable properties like de-centralized, requiring no trust in any single entity; Quick payments as quantum communication, regardless of the total number of users to speed up the transactions. In addition, fortis ámyna handles the situation in lighting speed in quantum bank-transaction if failed or has error due to connectivity, helping the initiator to track back the transaction from origin. This fortis ámyna model has tremendous advantages regarding speed, security, and authentication, guarding against quantum breaches. Ensuring more authenticity to customers and creating by linking a Distinct (De-Centralized) Identifier with blockchain’s current transaction state eventually leads to the updated digital ledger.
Students perception on the impact of their involvement in the learning process: an empirical study

Nowadays, when the changes that appear in programming paradigms and in software process development methodologies are extremely frequent, teaching a Software Engineering related course has become a demanding task. To all these are added changes caused by the dynamics of the society and the traits of the current learners and how they learn.
Adaptation Conflicts of Heterogeneous Devices in Iot Smart-Home

A promising technology such as Internet-of-Things have been introduced into traditional homes, buildings and cities to become smart and offer a wide range of services to simplify and enhance people’s lifestyle, a complex rule structure with a large number of sensing and actuating devices increases the chances of creating rules with faulty behaviors. Detection of sophisticated conflicts in an IoT system is one example of such faulty systems. In this paper, a mechanism is presented to detect such sophisticated conflicts among multi-resident smart-home services. Formally a model considering the functional properties of devices to distinguish a specific new kind of conflicts among the other basic types. Service User Regularity (SUR) conflict detection algorithm is proposed to trace resident habitual usage and behaviour conflicts and regulate them within the rules of the smart-home IoT-system. The system achieved good result; it could detect a reasonable number of targeted type conflicts within a synthesized data set.
CNN-Based Classification of Degraded Images Without Sacrificing Clean Images

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.
POS Tagging and NER System for Kannada Using Conditional Random Fields

Parts-of-speech (POS) tagging is a method used to assign the POS tag for every word present in the text, and named entity recognition (NER) is a process to identify the proper nouns in the text and to classify the identified nouns into certain predefined categories. A POS tagger and a NER system for Kannada text have been proposed utilizing conditional random fields (CRFs). The dataset used for POS tagging consists of 147K tokens, where 103K tokens are used for training and the remaining tokens are used for testing. The proposed CRF model for POS tagging of Kannada text obtained 91.3% of precision, 91.6% of recall, and 91.4% of f-score values, respectively. To develop the NER system for Kannada, the data required is created manually using the modified tag-set containing 40 labels. The dataset used for NER system consists of 16.5K tokens, where 70% of the total words are used for training the model, and the remaining 30% of total words are used for model testing. The developed NER model obtained the 94% of precision, 93.9% of recall, and 93.9% of F1-measure values, respectively.
Adaptive image inpainting algorithm based on sample block by kriging pretreatment and facet model

We present an improved Criminisi image inpainting algorithm through kriging pretreatment and facet model. We propose three improvement strategies. First, we propose an improved priority. A priority used in Criminisi algorithm consists of confidence term and data term. As iteration progresses, the confidence term is updated and plays a key role in the confidence term of the next edge block. At the same time, the confidence term gradually tends to 0, leading to a weakening of the role of priority, and ultimately affecting the repair effect. We propose an improved priority, which is represented by a piecewise function. Importantly, this improved priority reduces the risk of being weakened during iteration. Second, Criminisi algorithm uses fixed-size sample blocks to repair damaged images, regardless of whether the image content is a textured area or a structure area. We introduce an adaptive method to select sample block size based on the facet model. The size of the sample block is adaptively adjusted for different image contents, thereby improving the quality of the repaired image. Third, we show a weighted sum of squares differences matching principle based on the facet model. The matching formula is determined by the pixel gray value of a sample block and the structure value of four directions, which improves matching accuracy between target block and optimal matching block. Finally, experimental results show that the proposed algorithm is competitive with some state-of-the-art inpainting techniques in terms of both objective metrics and subjective visual inspection.
An Improved Two-stream Inflated 3D ConvNet for Abnormal Behavior Detection

Abnormal behavior detection is an essential step in a wide range of application domains, such as smart video surveillance. In this study, we proposed an improved two-stream inflated 3D ConvNet network approach based on probability regression for abnormal behavior detection. The proposed approach consists of four parts: (1) preprocessing pretreatment for the input video; (2) dynamic feature extraction from video streams using a two-stream inflated 3D (I3D) ConvNet network; (3) visual feature transfer into a two-dimensional matrix; and (4) feature classification using a generalized regression neural network (GRNN), which ultimately achieves a probability regression. Compared with the traditional methods, two-stream I3D feature extraction technology is better able to extract visual features and retain the optical flow and red, green and blue (RGB) information in the video. Probabilistic regression technology is better able to quantify data and provide a more intuitive visual experience. The experimental results on 50 detection cases from the UCF-Crime dataset show that the developed model obtains a high average abnormal behavior recognition accuracy. The improved I3D models obtain an average accuracy of 93.7% based on UCF-101, which outperforms the state-of-the-art methods, verifying the robustness and effectiveness of our approach.
Human-System Interface with Explanation of Actions for Autonomous Anti-UAV Systems

Research on explanation is currently of intense interest as documented in the DARPA 2021 investments reported by the USA Department of Defense. An emerging theme for explanation techniques research is their application to the improvement of human-system interfaces for autonomous anti-drone or C-UAV defense systems. In the present paper a novel proposal based on natural language processing technology concerning explanatory discourse using relations is briefly described. The proposal is based on the use of relations pertaining to the possible malicious actions of an intruding alien drone swarm and the defense decisions proposed by an autonomous anti-drone system. The aim of such an interface is to facilitate the supervision that a user must exercise on an autonomous defense system in order to minimize the risk of wrong mitigation actions and unnecessary spending of ammunition.
Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping

Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step toward building more brain-like artificial intelligence systems. Recent efforts in this direction use neural predictivity , or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a non-linear function, and measuring the error between the predicted and actual brain responses. Further, we propose to use a neural network to approximate this mapping function by training it on a set of neural recordings. The proposed method was implemented within the TensorFlow framework and evaluated on a suite of 8 state-of-the-art image recognition ANNs. Our experiments suggest that the use of a non-linear mapping function leads to higher neural predictivity. Our findings also reaffirm the observation that the latest advances in classification performance of image recognition ANNs are not matched by improvements in their neural predictivity. Finally, we examine the impact of pruning, a widely used ANN optimization, on neural predictivity, and demonstrate that network sparsity leads to higher neural predictivity.
User-Centred Agile Development to Support Sensemaking

User-centred design (UCD) is a fitting approach for supporting sensemaking, as the aim of UCD is to understand contextual user needs and use this as a basis to iteratively explore and design solutions. Similarly, agile software development uses iterations and rapid feedback to continuously explore, learn and improve user stories and their implementations. The user-centred and agile approaches represent mindsets of great influence in their respective fields of design and software development. In this chapter, we will briefly introduce the two approaches. We present five models that can be used 174to combine agile and user-centred development and discuss three common challenges in user-centred agile development (UCAD). Key take aways from the chapter are: The importance of exploring, understanding and framing the problem, in order to: Solve the right challenge. Propose solutions that fit the context of use. Make sure user needs drives ideation – not the love of technology. The importance of user involvement and contextual testing as a team effort to: Enable interdisciplinary decision-making and collaboration. Enable continuous learning and improvement based on feedback. Create solutions that work in real-life scenarios and contexts.
The Correlation Between the Use of Instagram and Female Adolescents’ Body Image

This Covid-19 situation has forced individuals to practice physical distancing and stay at home.This affects individuals' communication with each other.One of the means of communication that can be used for longdistance communication is social media or application.This thesis discusses the correlation between the intensity of Instagram usage and female adolescents' body image.The research methods used were descriptive and quantitative methods, using a correlational technique.The number of respondents who participated in this research is 184 people.The respondents were selected using purposive sampling technique, respondents are females in the age range of 18-22 and located in Jabodetabek area.The technique of data collecting is by using a questionnaire shared through Google Form to the respondents.The next step was to prove and analyze the data, so a variety of tests were performed, including validity test, reliability test, assumption testing that involves normality and linearity tests, and a hypothesis analysis was also conducted.Since this research uses a correlational technique, the assumption testing used are normality and linearity tests.The result of data processing of the normality test shows that the data are not normally distributed, with the result of 0.023 < 0.05 and 0.013 < 0.05.On the linearity test, the result is 0.386 > 0.05, so it can be concluded that the data are linear.In the calculation using Spearman correlation, the result is 0.997 and the score of sig. of 0.000 < 0.05, which means both variables in this research have a positive and significant correlation.
Data Science on Blockchains

Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. As blockchains and their applications proliferate, so do the complexity and volume of data stored by Blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in information sciences.
End-to-end Secured Fiber Optic Communication Using a Novel Encryption Algorithm

Fiber optic communication has grown in popularity in recent decades as a result of its several advantages over conventional cable communication. Fiber optic communication allows for a download speed of 100 Mb/s with minimal latency and a fast upload speed. Despite the fact that fiber optics can transfer enormous amounts of data swiftly over great distances, it is susceptible to a variety of security threats. It is simple to tap into the fiber optic cable. Fiber optic cable is a thin strand of glass or plastic which is used to transmit data from one place to other. A unique encryption method based on the Advanced Encryption Standard (AES) is used to provide end-to-end secure fiber optic communication. It employs a variable key cypher in which the original key is used to generate a sequence of AES sub keys, with feedback from previous cypher blocks, and each sub block is encrypted using multiple sub keys. The performance of the proposed encryption algorithm is simulated and examined. The simulation results suggest that the proposed method in fiber optic communication delivers strong end-to-end security.
Multi-Objective Emperor Penguin Optimizer for Tuning the Quality-of-Service in Cloud Computing

In the present situation, researchers have the task of developing a high quality features based cloud computing system in the real world if service is desired on request. Quality of service (QoS) describes such parameters which carry out the non-functional activities for any real time based systems. Such services are normally sufficient to achieve these QoS. Most of the information technology (IT) companies offer different cloud based services to its customers. There is no standard methodology available for calculating the actual use of cloud based resources and determining the rankings. From the customers’ point of view, it is not an easy task to find the reliable service provider that meets customer’s needs. This paper presents the QoS parameter especially on ‘throughput’ using Cloud Rank 3 with aid of Multi-Objective Emperor Penguin Optimizer (MOEPO) approach. This approach helps in finding the adorable and competent cloud service provider (CSP) available in the market. The different experiments have been carried out with actual QoS results, using Amazon EC2 services and its comparison.
Digital products with $ PN_k $-adjacencies and the almost fixed point property in $ DTC_k^\blacktriangle $

&lt;abstract&gt;&lt;p&gt;Given two digital images $ (X_i, k_i), i \in \{1, 2\} $, first of all we establish a new $ PN_k $-adjacency relation in a digital product $ X_1 \times X_2 $ to obtain a relation set $ (X_1 \times X_2, PN_k) $, where the term $ ''$$ PN $" means $ ''$pseudo-normal". Indeed, a $ PN $-$ k $-adjacency is softer or broader than a normal $ k $-adjacency. Next, the present paper initially develops both $ PN $-$ k $-continuity and $ PN $-$ k $-isomorphism. Furthermore, it proves that these new concepts, the $ PN $-$ k $-continuity and $ PN $-$ k $-isomorphism, need not be equal to the typical $ k $-continuity and a $ k $-isomorphism, respectively. Precisely, we prove that none of the typical $ k $-continuity (&lt;italic&gt;resp.&lt;/italic&gt; typical $ k $-isomorphism) and the $ PN $-$ k $-continuity (&lt;italic&gt;resp.&lt;/italic&gt; $ PN $-$ k $-isomorphism) implies the other. Then we prove that for each $ i \in \{1, 2\} $, the typical projection map $ P_i: X_1 \times X_2 \to X_i $ preserves a $ PN_k $-adjacency relation in $ X_1 \times X_2 $ to the $ k_i $-adjacency relation in $ (X_i, k_i) $. In particular, using a $ PN $-$ k $-isomorphism, we can classify digital products with $ PN_k $-adjacencies. Furthermore, in the category of digital products with $ PN_k $-adjacencies and $ PN $-$ k $-continuous maps between two digital products with $ PN_k $-adjacencies, denoted by $ DTC_k^\blacktriangle $, we finally study the (almost) fixed point property of $ (X_1 \times X_2, PN_k) $.&lt;/p&gt;&lt;/abstract&gt;
Mapping Critical Minerals from the Sky

Critical mineral resources titanium, zirconium, and rare earth elements occur in placer deposits over vast parts of the U.S. Atlantic Coastal Plain.Key questions regarding provenance, pathways of minerals to deposit sites, and relations to geologic features remain unexplained.As part of a national effort to collect data over regions prospective for critical minerals, the first public high-resolution aeroradiometric survey over the U.S. Atlantic Coastal Plain was conducted over Quaternary sediments in South Carolina.The new data provide an unprecedented view of potential deposits by imaging Th-bearing minerals in the heavy mineral assemblage.Sand ridges show the highest radiometric Th values with localized, linear anomalies, especially along the shoreface and in areas reworked by multiple processes and/or during multiple episodes.Estuarine areas with finer-grained sediments show lower, distributed Th anomalies.Th values averaged over geologic unit areas are similar for both environments, suggesting that heavy minerals are present but have not been locally concentrated in the lower-energy estuarine environments.Radiometric K highlights immature minerals such as mica and potassium feldspar.K is elevated within shallow sediments younger than ca.130 ka, an attribute that persists in regional data from northern South Carolina to northern Florida.Both K and Th are elevated over the floodplains of the Santee River and other rivers with headwaters in the igneous and metamorphic Piedmont Terrane.The persistence of K anomalies for distances of more than 100 km from the Santee River floodplain suggests that heavy minerals are delivered from the Piedmont to offshore areas by major rivers, transported along the coast by the longshore current, and redeposited onshore by marine processes.