Reviewerly/exHarmony huggingface dataset:
Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Personalized recommendation is one of the important contents of personalized service in university libraries. Accurate and in-depth understanding of users is the premise of personalized recommendation. This paper proposes a personalized book recommendation algorithm based on deep learning models according to the characteristics and laws of user savings in university libraries. The method first uses the long short-term memory network (LSTM) to improve the deep autoencoder (DAE) so that the model can extract the temporal features of the data. Then, the Softmax function is used to obtain the book recommendation result of the current user. The proposed method is verified based on actual library lending data. The experimental results show that the proposed method has performance advantages compared with several existing recommendation methods.
The Convergence of Hyperscale Data Center and High-Performance Computing Networks

We discuss the differences and commonalities between network technologies used in supercomputers and data centers and outline a path to convergence at multiple layers. We predict that emerging smart networking solutions will accelerate that convergence.
Dihedral Lattice Gauge Theories on a Quantum Annealer

We study lattice gauge theory with discrete, non-Abelian gauge groups. We extend the formalism of previous studies on D-Wave's quantum annealer as a computing platform to finite, simply reducible gauge groups. As an example, we use the dihedral group $D_n$ with $n=3,4$ on a two plaquette ladder for which we provide proof-of-principle calculations of the ground-state and employ the known time evolution formalism with Feynman clock states.
Improved bias value and new membership function to enhance the performance of fuzzy support vector Machine

The effect of an outlier in classification is a problem in the Fuzzy Support Vector Machine (FSVM). In this paper, two new methodologies have been implemented to compute fuzzy membership values for the fuzzy data to address this issue. The membership values that arrived through these methods have been inputted in a fuzzy support vector machine. A method has been devised to select the best bias value to bring out the classifier using the bias values obtained through the support vectors. The Geometric Mean (Gm) and consistency in the newly proposed method for selecting bias values are used as the criteria to choose the best bias value. The methods such as average bias value, individual bias value, weighted average bias value, and median bias value are considered for this purpose. The obtained classifier improves the classification accuracy by reducing the fuzziness in data. The relation between the new membership function and the slack variable ξi has been exposed in terms of how new membership function reduces the chances of ξi become a basic variable. An experiment has been conducted with seventeen datasets, including fourteen real-world datasets. A comparison of the performance of the proposed Membership function with other membership functions is made. The robustness of the proposed membership functions for outliers is verified. A statistical test has been performed to ensure the significance of the proposed model.
Only-train-electrical-to-optical-conversion (OTEOC): simple diffractive neural networks with optical readout

Machine learning hardware based on optical diffraction is emerging as a new computing platform with high throughput and low latency. The current all-optical diffractive deep neural networks often suffer from complex optical configuration, lack of efficient optical nonlinear activation, and critical alignment between optical layers for system integration. The opto-electronic diffractive neural networks can partially address these issues by shifting some computation load, e.g., nonlinear activation and adaptive training, to the electronic domain. However, these hybrid networks require extra optical-to-electrical conversion that inevitably slows the overall process down. Here, we propose a simple opto-electronic diffractive neural network with just one optical layer enabled by a standard phase-only spatial light modulator. The proposed system can classify images by optical readout and does not need to collect the light distribution for subsequent electronic computation. The nonlinear function is intrinsically integrated in the essential encoding process from the electronic input to the modulated wavefront of light. Thanks to its simplicity, the system can reach high classification accuracy without calibration and can be reconfigured by updating the weights without changing or moving any physical component. We believe this technology brings diffractive neural networks a step closer to building realistic optics-based neurocomputers.
Nufix

Developers usually suffer from <u>d</u>ependency <u>m</u>aze (DM) issues, i.e., package dependency constraints are violated when a project's platform or dependencies are changed. This problem is especially serious in .NET ecosystem due to its fragmented platforms (e.g., .NET Framework, .NET Core, and .NET Standard). Fixing DM issues is challenging due to the complexity of dependency constraints: multiple DM issues often occur in one project; solving one DM issue usually causes another DM issue cropping up; the exponential search space of possible dependency combinations is also a barrier.
PARTAS: A Personalizable Augmented Reality Based Task Adaption System for Workers with Cognitive Disabilities

Commercially available assembly systems for industrial settings do not address the needs and requirements of people with disabilities and sheltered workshops. This is due to the complexity of the system’s setup including hardware, software and application in combination with the workers’ and supervisors’ capabilities. In the case of sheltered workshops, resources for implementing functionalities and accessibility requirements push expenses beyond available resources. In this work, we present a prototype of an intuitive, adaptive and cost-efficient worker assistance system utilizing only one RGB camera, one projector and a small single-board computer. The system implements a combination of projected, contour-based instructions and pick-by-projection functionality with the goal of maximizing the instructions’ affordance. An interdisciplinary team including workers from a sheltered workshop, their caregivers, technicians and a psychologist followed an iterative user-centered design methodology approach based on two personas with different cognitive disability profiles. Finally, we conducted a user study based on eight participants matching the main persona. All participants showed a strong learning rate and performed successfully completely new assembly tasks after a short training phase with the proposed system.
Deep Learning Shape Trajectories for Isolated Word Sign Language Recognition

In this paper, we propose an efficient trajectories analysis solution for the recognition of Isolated Word Sign Language (IWSL). The key technique innovation in this work is the shape trajectories analysis based on the deep learning method and achieved impressive results on different IWSL data sets: German: Rheinisch Westfälische Technische Hochschule(RWTH): RWTH-Boston-50 and RWTH-Boston-104(95.83%), Signer-Independent Continuous Sign Language Recognition for Large Vocabulary Using Subunit Models (SIGNUM: 98.21%) and new Tunisian Sign Language database (TunSigns: 98%).
The Challenges and Opportunities for Developing the Use of Data and Artificial Intelligence (AI) in North Africa: Case of Morocco

Artificial intelligence (AI) represents a significant potential for the African continent. AI can drive progress, development, and democratization governments properly handle the challenges. It can boost productivity growth by extending opportunities in crucial African development areas such as agriculture, healthcare, financial services, and public services. AI will enable employees, entrepreneurs, and enterprises to compete worldwide and be at the vanguard of economic development by providing access to high-quality digital tools. However, the roadblocks necessitate firm policy answers. AI will need significant changes for workers, employers, businesses and open new ethical questions that need thoughtful responses. Higher constraints specific to Africa, such as network limits, educational institution readiness, and the availability of digital data, exacerbate the ethical issues. Africa must make aggressive attempts to address its problems; however, if it succeeds, it will catch up to countries that have previously taken steps to improve AI. These efforts will be complicated, but the road ahead is clear. The government’s effectiveness will be determined by its capacity to facilitate collaboration among all stakeholders, including state and civil society, academics, industry, and national and international stakeholders.
Profiled Side-Channel Attack on Cryptosystems Based on the Binary Syndrome Decoding Problem

The NIST standardization process for post-quantum cryptography has been drawing the attention of researchers to the submitted candidates. One direction of research consists in implementing those candidates on embedded systems and that exposes them to physical attacks in return. The <i>Classic McEliece</i> cryptosystem, which is among the four finalists of round 3 in the Key Encapsulation Mechanism category, builds its security on the hardness of the syndrome decoding problem, which is a classic hard problem in code-based cryptography. This cryptosystem was recently targeted by a laser fault injection attack leading to message recovery. Regrettably, the attack setting is very restrictive and it does not tolerate any error in the faulty syndrome. Moreover, it depends on the very strong attacker model of laser fault injection, and does not apply to optimised implementations of the algorithm that make optimal usage of the machine words capacity. In this article, we propose a to change the angle and perform a message-recovery attack that relies on side-channel information only. We improve on the previously published work in several key aspects. First, we show that side-channel information, obtained with power consumption analysis, is sufficient to obtain an integer syndrome, as required by the attack framework. This is done by leveraging classic machine learning techniques that recover the Hamming weight information very accurately. Second, we put forward a computationally-efficient method, based on a simple dot product and information-set decoding algorithms, to recover the message from the, possibly inaccurate, recovered integer syndrome. Finally, we present a masking countermeasure against the proposed attack.
Exploring Security Threats on Blockchain Technology along with possible Remedies

Being the center of commotion in recent times, Blockchain Technology is on its way to bloom in the technological world. With countless applications, even governments and health facilities are adopting blockchain technology into their applications because of the reliability and security it is supposed to provide. Not to mention how it bought about revolution in currency by making it possible for crypto-currency to exist as introduced by Satoshi Nakamoto in his ground-breaking paper. Unfortunately like every other technology in existence, blockchain is also potentially vulnerable no matter how secure it was meant to be designed. We will take a look at such potential threats to the blockchain technology in this paper, from seemingly human factors to some technical aspects that are not too deep to understand for anyone. We will also discuss about the possible remedies to the explored threats to the blockchain technology.
Conditional GANs based system for fibrosis detection and quantification in Hematoxylin and Eosin whole slide images

Assessing the degree of liver fibrosis is fundamental for the management of patients with chronic liver disease, in liver transplants procedures, and in general liver disease research. The fibrosis stage is best assessed by histopathologic evaluation, and Masson’s Trichrome stain (MT) is the stain of choice for this task in many laboratories around the world. However, the most used stain in histopathology is Hematoxylin Eosin (HE) which is cheaper, has a faster turn-around time and is the primary stain routinely used for evaluation of liver specimens. In this paper, we propose a novel digital pathology system that accurately detects and quantifies the footprint of fibrous tissue in HE whole slide images (WSI). The proposed system produces virtual MT images from HE using a deep learning model that learns deep texture patterns associated with collagen fibers. The training pipeline is based on conditional generative adversarial networks (cGAN), which can achieve accurate pixel-level transformation. Our comprehensive training pipeline features an automatic WSI registration algorithm, which qualifies the HE/MT training slides for the cGAN model. Using liver specimens collected during liver transplantation procedures, we conducted a range of experiments to evaluate the detected footprint of selected anatomical features. Our evaluation includes both image similarity and semantic segmentation metrics. The proposed system achieved enhanced results in the experiments with significant improvement over the state-of-the-art CycleGAN learning style, and over direct prediction of fibrosis in HE without having the virtual MT step.
A Federated Mining Approach on Predicting Diabetes-Related Complications: Demonstration Using Real-World Clinical Data.

Chronic diabetes can lead to microvascular complications, including diabetic eye disease, diabetic kidney disease, and diabetic neuropathy. However, the long-term complications often remain undetected at the early stages of diagnosis. Developing a machine learning model to identify the patients at high risk of developing diabetes-related complications can help design better treatment interventions. Building robust machine learning models require large datasets which further requires sharing data among different healthcare systems, hence, involving privacy and confidentiality concerns. The main objective of this study is to design a decentralized privacy-protected federated learning architecture that can deliver comparable performance to centralized learning. We demonstrate the potential of adopting federated learning to address the challenges such as class-imbalance in using real-world clinical data. In all our experiments, federated learning showed comparable performance to the gold-standard of centralized learning, and applying class balancing techniques improved performance across all cohorts.
Cloud computing security analysis based on RC6, AES and RSA algorithms in user-cloud environment

In this paper a cloud computing security analysis based on RC6, AES and RSA algorithms in client-cloud environment has proposed. This framework is divided in 2 phases. Data pre-processing is first phase applied based on the weight assigned. Based on the data values obtainable from the uploaded textual file in cloud environment weight assigned is calculated. Now in second phase fuzzy c-means has been applied in cloud on the uploaded data for clustering. First the score in terms of aggregated assessment ranking have been calculated based on simple additive weighting (SAW). Then the decision performances have been arranged. Different random sets of data values have been obtained and also the aggregated values have been obtained. These aggregated sets will determine the security operation and the risk associated with the data uploaded with the cloud environment. The maximum key unpredictability and validation shows the privileged security in terms of the elevated threat opportunity. Proposed automated FCM based clustered and multi layer security standard algorithms have potential to deal with multifaceted security.
Medical image contrast enhancement based on improved sparrow search algorithm

Abstract The collection or transmission of medical images is often disturbed by various factors, such as insufficient brightness and noise pollution, which will result in the deterioration of image quality and significantly affect the clinical diagnosis. To improve the quality of medical images, a contrast enhancement method based on improved sparrow search algorithm is proposed in this paper. The method is divided into two steps to enhance the medical images. First, a new transform function is introduced to improve the brightness or contrast of medical images, and two parameters in the transform function are optimized by the improved sparrow search algorithm. Second, adaptive histogram equalization method with contrast limited is used to equalize the result image of the previous step to make the pixel distribution of the image more uniform. Finally, a large number of experiments and qualitative and quantitative analyses were conducted on the common data sets. The analysis results demonstrate that the presented approach outperforms some existing medical image processing approaches.
The New Rewriting Engine of Dedukti

Dedukti is a type-checker for the $\lambda$$\Pi$-calculus modulo rewriting, an extension of Edinburgh's logicalframework LF where functions and type symbols can be defined by rewrite rules. It thereforecontains an engine for rewriting LF terms and types according to the rewrite rules given by the user.A key component of this engine is the matching algorithm to find which rules can be fired. In thispaper, we describe the class of rewrite rules supported by Dedukti and the new implementation ofthe matching algorithm. Dedukti supports non-linear rewrite rules on terms with binders usinghigher-order pattern-matching as in Combinatory Reduction Systems (CRS). The new matchingalgorithm extends the technique of decision trees introduced by Luc Maranget in the OCamlcompiler to this more general context.
Knowledge Graph Embeddings and Explainable AI

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.
Visual Parameter Selection for Spatial Blind Source Separation

Abstract Analysis of spatial multivariate data, i.e., measurements at irregularly‐spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non‐expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.
The Role of Reusable and Single-Use Side Information in Private Information Retrieval

This paper introduces the problem of Private Information Retrieval with Reusable and Single-use Side Information (PIR-RSSI). In this problem, one or more remote servers store identical copies of a set of K messages, and there is a user that initially knows M of these messages, and wants to privately retrieve one other message from the set of K messages. The objective is to design a retrieval scheme in which the user downloads the minimum amount of information from the server(s) while the identity of the message wanted by the user and the identities of an M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -subset of the M messages known by the user (referred to as reusable side information) are protected, but the identities of the remaining M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> :=M−M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> messages known by the user (referred to as single-use side information) do not need to be protected. The PIR-RSSI problem reduces to the classical Private Information Retrieval (PIR) problem when M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> =M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> = 0, and reduces to the problem of PIR with Private Side Information or PIR with Side Information when M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ≥ 1, M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> = 0 or M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 0, M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ≥ 1, respectively. In this work, we focus on the single-server setting of the PIR-RSSI problem. We characterize the capacity of this setting for the cases of M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 1, M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ≥ 1 and M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ≥ 1, M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> = 1, where the capacity is defined as the maximum achievable download rate over all PIR-RSSI schemes. Our results show that for sufficiently small values of K, both the single-use and reusable side information messages can help in reducing the download cost; and for larger values of K, only the single-use side information messages can help in reducing the download cost.