Content Addressable Research Articles

Rational Redesign of an Introductory Pharmacy Practice Experience Curriculum

ObjectiveTo utilize a process to identify strengths and weaknesses of an introductory pharmacy practice experience (IPPE) curriculum from stakeholder perspectives and undergo IPPE curricular revision. MethodsAn IPPE curriculum redesign taskforce was created, and a 5-step systematic quality improvement process was developed and applied to redesign an IPPE curriculum. The steps were to identify existing curriculum challenges and strengths, determine potential solutions to challenges, redesign IPPE curricular structure, obtain stakeholder input and support, and redesign IPPE content. Throughout these steps, surveys were administered, and feedback was solicited from stakeholder groups through focus groups and meetings. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using inductive content analysis and peer debriefing to identify themes. ResultsStudent survey and focus group results identified desires to limit student-preceptor negotiations when scheduling hours, decrease conflict between IPPE hours and scheduled classes, and increase direct patient care opportunities. Structural revisions included transition of IPPE hours from the first and second year of the program into the third year, revising course schedule grids to allow third-year students 1 day per week to complete hours, and aligning rotation dates during class-free times. Curricular content was strengthened through curricular mapping, threading, and course coordinator collaborations. ConclusionA 5-step IPPE redesign systematic quality improvement process utilizing solicitation, analysis, and incorporation of stakeholder feedback was used to revise an IPPE curriculum to maintain framework and content strengths and address weaknesses. Other pharmacy programs could utilize this process to redesign their IPPE curriculum.

A Novel Ferroelectric FET Based Universal Content Addressable Memory With Reconfigurability for Area- and Energy-Efficient In-Memory-Searching System

A Novel Ferroelectric FET Based Universal Content Addressable Memory With Reconfigurability for Area- and Energy-Efficient In-Memory-Searching System

Web service reliability and scalability determination using optimized depth wise separable convolutional neural network

AbstractWeb service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively.

Secure Digital Signature Using Block Chain Technology

The lack of authentication and ant forge mechanisms severely compromises the security details of the authorizations issuing the certification. We employ blockchain technology to verify people in a way that is akin to a digital signature with their identity and access authorization, so resolving the issue of certificate forgery. Blockchain technology is an open distributed ledger that ensures that every transaction cannot be altered and holds unquestionable information in a highly secure and encrypted manner. A high standard for the procedure that may guarantee that the data in such a certificate is authentic indicates that the document is authentic and has not been forged, having come from a reliable source. The interplanetary file system uses the content address as the only means of uniquely identifying every file inside a global namespace that includes all computing devices. A bi-dimensional barcode that provides data as black and white dots is called a Quick Response (QR) code. The system consists of black squares that can be photographed by a camera or other image device, arranged in a square framework on a white background.

Novel machine learning based authentication technique in VANET system for secure data transmission

<p>Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges in vehicular ad hoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system (IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology which attempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combined technology, the source metadata information collected from the inter planetary file system is stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralized vehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.</p>

DDCM: a decentralized density clustering and its results gathering approach

The use of distributed clustering is an important method of solving large-scale data mining problems. There are still some problems associated with distributed clustering, such as a performance bottleneck on the master node and network congestion caused by global broadcasting. This paper proposes a decentralized clustering method based on density clustering and the content-addressable network technique. It can form a cluster with excellent scalability and load balancing capabilities based on several surrounding nodes. In addition, a method is presented for optimizing the way clustering results are gathered in different application scenarios. Based on our extensive experiments, the proposed approach performs three times better than benchmark algorithms in terms of efficiency and has a stable expanding ratio of about 0.6 for large-scale data sets.

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities. So, the SDN must handle traffic from across the whole network. The flow table depends on Ternary Content Addressable Memorable Memory (TCAM) for storing and a quick search of regulations; it is restricted in capacity owing to its elevated cost and energy consumption. Whenever the flow table is abused and overflowing, the usual regulations cannot be executed quickly. In this case, we consider low-rate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate. This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN, using Feed Forward Neural Network (FFNN), K-Means, and Decision Tree (DT). We generate two network topologies, Fat Tree and Simple Tree Topologies, with the Mininet simulator and coupled to the OpenDayLight (ODL) controller. The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query, propagation delay, overall dropped packets, energy consumption, bandwidth usage, latency rate, and throughput. The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network. By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations, the evaluation tool examines every flow against a set of criteria. The FFNN with DT and K-means algorithms obtain accuracies of 96.29% and 97.51%, respectively, in the identification of collision flows, according to the experimental outcome when associated with existing methods from the literature.

CapCAM: A Multilevel Capacitive Content Addressable Memory for High-Accuracy and High-Scalability Search and Compute Applications

As one type of associative memory, content-addressable memory (CAM) has become a critical component in several applications, including caches, routers, and pattern matching. Compared with the conventional CAM that could only deliver a “matched or not-matched” result, emerging multilevel CAM (ML-CAM) is capable of delivering “the degree of match” with multilevel distance calculation. This feature has been desired in applications that need beyond-Boolean matching results. However, existing ML-CAM designs are limited by the bit-cell device discharging current mismatch and vulnerability to the timing of sensing operations for distance calculation. This inherent constraint makes it difficult to further improve the accuracy and scalability toward higher accuracy and higher dimension matching. In this work, we propose CapCAM, a multilevel Capacitive Content Addressable Memory. It could be implemented based on either static random-access memory (SRAM) or emerging technologies, e.g., the ferroelectric field-effect transistor (FeFET). CapCAM could provide linear and stable voltage drop scaled by the match degree and need no strict timing for result sensing, which embraces the high-accuracy and high-scalability search. The inherent enabler of CapCAM is the charge-domain computing mechanism. This article will present the basic concept, operating mechanisms, detailed circuit designs, and circuit-level simulations of CapCAM. Besides, we apply CapCAM to few-shot learning applications and compare CapCAM with the current-domain TCAM designs. Results show 99.2% accuracy for a five-way five-shot classification task with our proposed CapCAM design while considering 1-fF capacitors, 20-domain FeFETs, and 256 columns. In contrast, the prior work based on discharging dynamics requires strict timing controls and suffers from accuracy degradation under the same configuration, which demonstrates CapCAM’s capability of low-power, accurate, and scalable multilevel CAM (ML-CAM) computing.

GeNVoM: Read Mapping Near Non-Volatile Memory.

DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS) revolutionized genomic research. As a result, modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel. The sequenced DNA fragments, representing the output of NGS platforms, are termed reads. Besides genomic variations, NGS imperfections induce noise in reads. Mapping each read to (the most similar portion of) a reference genome of the same species, i.e., read mapping, is a common critical first step in a diverse set of emerging bioinformatics applications. Mapping represents a search-heavy memory-intensive similarity matching problem, therefore, can greatly benefit from near-memory processing. Intuition suggests using fast associative search enabled by Ternary Content Addressable Memory (TCAM) by construction. However, the excessive energy consumption and lack of support for similarity matching (under NGS and genomic variation induced noise) renders direct application of TCAM infeasible, irrespective of volatility, where only non-volatile TCAM can accommodate the large memory footprint in an area-efficient way. This paper introduces GeNVoM, a scalable, energy-efficient and high-throughput solution. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, GeNVoM rethinks the algorithm and non-volatile TCAM-based accelerator design together from the ground up. Thereby GeNVoM can improve the throughput by up to 3.67×; the energy consumption, by up to 1.36×, when compared to an ASIC baseline, which represents one of the highest-throughput implementations known.

A Reliable High-speed Compact In-memory Matching Circuit for CAM-Application Based on NV-RAM

This paper presents an effective approach forimplementing content address memory (CAM) based on Nonvolatile random-access memory (NV-RAM) technologies. Weused the 2T-2R bitcell structure implemented on a 65nmCMOS process with a special in-memory matching circuitfor realizing low-delay and energy-efficient lookup operations.The simulation results on Synopsys HSPICE indicate that theproposed CAM design can achieve a search error rate of0.03-4.61%, search energy per bit of 4.36-6.47 fJ, and anextremely small search latency varying from 0.11-0.12 nsdepending on the specific design configurations.

In-Memory Search With Phase Change Device-Based Ternary Content Addressable Memory

Here, we proposed an in-memory search prototype based on phase change memory (PCM). First, using the PCM, a highly compact (8F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and low-energy (0.3 fJ/bit/search) nonvolatile ternary addressable memory (TCAM) is demonstrated and achieved a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.6\times $ </tex-math></inline-formula> energy saving and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50\times $ </tex-math></inline-formula> cell area saving over the 16T SRAM-TCAM. Thanks to the non-volatility and massive parallelism of the PCM TCAM to computing Hamming distance, the frequent memory access is alleviated, enabling search operation in situ. The prototype shows a high throughput of 256 GB/s in data deduplication application, obtaining more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$250\times $ </tex-math></inline-formula> energy saving and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$42\times $ </tex-math></inline-formula> improvement in throughput over CPU. Our study provides a low-area-overhead, low-energy, and high-throughput solution for the storage system to perform search in situ.

Gate All around CNTFET based Ternary Content Addressable Memory

This paper proposes a design of gate all around CNTFET based ternary content addressable memory (TCAM). TCAM cell is designed and simulated in HSPICE using top gated CNTFET (TG-CNTFET) & gate all around CNTTFET (GAA-CNTFET). Dual chirality technique is used to design TCAM cell i.e. different chirality for n-type CNTFET & p-type CNTFET which utilizes different threshold voltages and hence improve the performance. Comparative analysis has been done for various parameters viz. Static noise margin (SNM), delay, power and power delay product (PDP). It has been found that GAA-CNTFET based TCAM gives better SNM and power as compared to TG-CNTFET based TCAM, however TG-CNTFET based TCAM gives less delay. However overall PDP is less in GAA-CNTFET which make is suitable for circuit applications as compared to TG-CNTFET.

Arabic Fake News Detection: A Fact Checking Based Deep Learning Approach

Fake news stories can polarize society, particularly during political events. They undermine confidence in the media in general. Current NLP systems are still lacking the ability to properly interpret and classify Arabic fake news. Given the high stakes involved, determining truth in social media has recently become an emerging research that is attracting tremendous attention. Our literature review indicates that applying the state-of-the-art approaches on news content address some challenges in detecting fake news’ characteristics, which needs auxiliary information to make a clear determination. Moreover, the ‘Social-context-based’ and ‘propagation-based’ approaches can be either an alternative or complementary strategy to content-based approaches. The main goal of our research is to develop a model capable of automatically detecting truth given an Arabic news or claim. In particular, we propose a deep neural network approach that can classify fake and real news claims by exploiting ‘Convolutional Neuron Networks’. Our approach attempts to solve the problem from the fact checking perspective, where the fact-checking task involves predicting whether a given news text claim is factually authentic or fake. We opt to use an Arabic balanced corpus to build our model because it unifies stance detection, stance rationale, relevant document retrieval and fact-checking. The model is trained on different well selected attributes. An extensive evaluation has been conducted to demonstrate the ability of the fact-checking task in detecting the Arabic fake news. Our model outperforms the performance of the state-of-the-art approaches when applied to the same Arabic dataset with the highest accuracy of 91%.

SDN-Based Traffic Matrix Estimation in Data Center Networks through Large Size Flow Identification

Software defined networking (SDN) with separated control plane and data plane brings new opportunities for traffic measurement in data center networks. However, in the SDN-enabled switches, available TCAM (Ternary Content Addressable Memory) resources for traffic measurement are limited. Thus, it is necessary to utilize traffic matrix (TM) estimation to derive a hybrid network monitoring scheme through combining the partial direct measurement offered by SDN with some inference techniques. Although large size flows play an important role in improving TM estimation accuracy, directly monitoring each flow and finding out large size flows consume massive channel bandwidth resource between control plane and data plane. Therefore, in this paper, we identify large size flows from multiple historical TMs instead of monitoring each flow. First, we analyze multiple historical TMs and observe that origin-to-destination (OD) pair whose flow size is selected as large size flow at last time slot is most likely to be selected for per-flow monitoring at next time slot, so these OD pairs are identified by gradient boosting machine and are directly regarded as sampled OD pairs in order to reduce resource consumption. Then, we propose a greedy heuristic algorithm to solve SDN-enabled switch selection problem to best utilize the TCAM resources and guarantee that most of sampled OD pairs are measured in the flow table. We also present a source node prefix tree based bit merging aggregation (SPTBMA) scheme to design feasible forwarding rules to be inserted in TCAM of SDN-enabled switches and reserve more TCAM space for sampled OD pairs. Finally, the experimental results based on real traffic dataset demonstrate that our proposed scheme outperforms the existing algorithms in terms of improving TM estimation accuracy and overcoming limitation of TCAM resources.

Activity and voice of people with sensory dysfunctions on social media. Report on netnographic research

Social media are becoming an essential communication tool for people with disabilities and can become a good resource of knowledge for social workers. As a critical area of contemporary visual culture, social media can enhance the empowerment and social inclusion of people with visual and hearing impairments. The aim of the presented online research conducted between March and July 2021 was to answer the main research question: what self-expression content people with sensory disorders present on social media. Additionally, specific research questions were posed: how the content address topics relates to social inclusion; what benefits can social workers gain from observing social media accounts run by people with visual and hearing impairments? To analyse the presence of people with visual and hearing impairment on social media, I used three qualitative research methods: desk research, netnography, and case study. The analysis of the collected material allowed me to distinguish nine main thematic categories of content published by people with sensory dysfunctions on social media: everyday life, medical information, modern technologies, forms of support, taboos, humour, social activity, relationships, leisure. The categories were generated through coding and categorization in an emic perspective. The analysis also indicated that much of the content discussed relates to the idea of social inclusion for people with sensory dysfunctions. The article also shows a series of benefits of observing social media for social workers in their professional practice.

Graph signal recovery using restricted Boltzmann machines

Given: (a) clean training dataset, (b) trained graph machine learning pipeline, and (c) noisy test dataset; how do we make the pipeline robust to such noise? In an attempt to answer this question, we propose a modification to the pipeline that exploits both the content addressability of a restricted Boltzmann machine and the message passing capabilities of a graph neural network. We investigate which of the hidden layers of the pipeline is best suited for our proposed modification. By establishing that a decrease in mutual information indicates an increase in prediction accuracy which is in turn driven by progressive geometric clustering of the samples belonging to the same class, we attempt to explain the behaviour exhibited by the modified pipeline. Empirical results demonstrate that our approach makes the pipeline robust to some real-world noisy scenarios in citation datasets.

AGFT: Adaptive entries aggregation scheme to prevent overflow in multiple flow table environment

AbstractThe revolutionary architecture termed Software‐Defined Network provides flexible network management by detaching the control logic from the underlying data plane. The flow table resides in Ternary Content Addressable Memory imparts rules for the incoming flows with a limitation of high cost, limited storage, and consumes high power. In data center networks, when the traffic rate is high the overflow occurs due to storage limitation with high packet drop, frequent rule miss, and severe controller overhead. To overcome these challenges and to provide Quality of Service to the current network design sketch‐based entry reduction scheme is proposed. It incorporates three concurrent modules integrated to function sequentially where (1) Periodical analysis in Multiple Flow Tables is performed to ensure the availability of redundant entries using the robust data mining algorithm called Term Frequency. (2) Recurrent entries are further classified and clustered using the Boyer–Moore pattern matching algorithm to facilitate the forthcoming aggregation process. (3) A compact flow table is achieved with a customized multibit trie using Huffman coding compression technique. The experimental outcomes prove that this work prevents overflow by 99.98% with 98.99% enhanced flow table space and provides a significant reduction of controller overhead than the existing schemes.

Instagram Content Addressing Pruritic Urticarial Papules and Plaques of Pregnancy: Observational Study.

Pruritic urticarial papules and plaques of pregnancy (PUPPP) is the most commonly diagnosed pregnancy-specific dermatosis. It presents with intense pruritus and can be difficult to manage, which encourages mothers to look to social media for camaraderie and advice. This study aimed to characterize the sources and thematic content of Instagram posts in order to define influential groups of users. Our goal was to determine the status of online discourse surrounding PUPPP and elucidate any potential space for health care provider intervention via creation of Instagram accounts dedicated to information dissemination for patient populations. Three hashtag categories were selected (#PUPPP, #PUPPPs, and #PUPPPrash), and the top public posts from each were analyzed and organized by source and by thematic content. The numbers of likes and comments were also recorded. Among the top 150 posts in each hashtag category, only 428 posts in total were eligible for this analysis. Majority (316/428, 73.8%) of posts were created by mothers who experienced PUPPP. These posts were testimonial accounts in nature. A small fraction of posts (14/428, 3.3%) were generated by physician accounts. Posts from blogs with extensive followings garnered the most attention in the form of likes and comments. Mothers experiencing PUPPP comprised the majority of accounts posting under the hashtags selected. The most common themes included pictures of the rash and personal testimonies. Posts under blog posts received the most likes and comments on average. There is space for physician and health care specialists to improve their social media presence when it comes to discourse surrounding PUPPP. Patients are seeking out communities on social media, like Instagram, in order to have questions answered and obtain advice on management. Accounts with large followings tend to have more likes and more comments, which encourages information dissemination and awareness. Thus, we suggest that physicians create content and potentially partner with blog-type accounts to improve outreach.

Low power CMOS based Self Controlled Precharge Free Content Addressable Memory with Minimum area for Image Processing Devices

Image processing devices plays a vital role in several applications like medical, security, biometric etc. The devices ranges from portable size to larger machines with and without Human Computer Interface possibilities. As the image processing and human computer interface system application requires higher memory requirements, the power and area should be small. Searching of data is a high priority work in image classification. To perform high speed search through hardware Content Addressable Memory is used. But the circuit suffers from higher power consumption, precharging issues and low performance. For longer word length the elimination of precharge is needed. So for high speed applications self-controlled precharge-free CAM (SCPF-CAM) is suitable. A 4T hybrid self controlled pre charge free Content Addressable Memory is proposed in this paper using CMOS 32nm technology. The observation shows that the circuit works at high speed, minimizes the search time and has high performance operation. When compared to the conventional SCPF-CAM, 8T CAM the proposed design reduces the number of transistors. The reduction in area is about approximately 20% and can be used in low power and low energy applications. In Synopsis HSPICE Predictive technology models were used for the implementation in 32nm CMOS technology. The work will be extended in future using FinFET technology where the leakage current can be minimized.

Improving dynamic service function chaining classification in NFV/SDN networks through the offloading concept

Service Function Chaining (SFC) paradigm improves network capabilities thanks to the support of application-driven-networking, which is realized through the invocation of an ordered set of Service Functions (SFs). The programmability and flexibility provided by emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are perfect features for efficiently managing the lifecycle of SFCs. However, the limitation of Ternary Content Address Memories (TCAMs) of the SDN nodes in SDN-based SFC scenarios can lead to network performance degradation when the SFC classifier is not able to install new classification rules. To tackle the Dynamic Chain Request Classification Offloading (D-CRCO) problem presented in this paper, a hybrid eviction and split-and-distribute approach is proposed, where i) the dynamic behavior of SFC requests is exploited by removing the corresponding idle rules from the flow tables if necessary; and ii) SFC classification is not forced to be carried out by the ingress node, but by any transit node in the domain. An Integer Linear Programming (ILP) formulation and an heuristic are provided to solve D-CRCO, with the goal of maximizing the number of SFC requests that can be served, respecting TCAM size, link capacity, and SF availability constraints. Simulation and emulation results over two real topologies show that the proposed solution is able to significantly increase the number of served SFC requests with a negligible impact on the network performance.