Reviewerly/exHarmony huggingface dataset:
A Dual-Mode Similarity Search Accelerator based on Embedding Compression for Online Cross-Modal Image-Text Retrieval

Image-text retrieval (ITR) that identifies the relevant images for a given text query, or vice versa, is the fundamental task in emerging vision-and-language machine learning applications. Recently, the cross-modal approach that extracts image and text features in separate reasoning pipelines but performs the similarity search on the same embedding representation is proposed for the real-time ITR system. However, the similarity search that finds the most relevant data in huge data embeddings for a given query becomes the bottleneck of the ITR system.In this paper, we propose a dual-mode similarity search accelerator that can solve the computational hurdle for online image-to-text and text-to-image retrieval service. We propose an embedding compression scheme that removes the sparsity in the text embeddings, further eliminating the time-consuming masking operations in the later processing pipeline. Combining with the data quantization from 32-bit floating-point to 8- bit integer, we reduce the target dataset size by 95.1% with less than 0.1% accuracy loss for 1024-dimensional embedding features. In addition, we propose a streamlined similarity search data flow for both query types, which minimizes the required memory bandwidth with maximal data reuse. The query and data embeddings are guaranteed to be fetched only once from the external memory with the optimized data flow. Based on the proposed data representation and flow, we design a scalable similarity search accelerator that includes multiple ITR kernels. Each ITR kernel has modular design, composed of a separate memory access module and a computing module. The computing module supports pipelined operations of the four similarity search tasks: dot product calculation, data reordering, partial score aggregation, and ranking. We double the number of processing operations in the computing module with the DSP packing technique. Finally, we implement the proposed accelerator with six ITR kernels on the Xilinx Alveo U280 FPGA card. It shows 2.98 tera operations per second (TOPS) performance at 186 MHz, achieving 526/144 and 1163/306 queries per second (QPS) performance for image-to-text and text-to-image retrieval on MS-COCO 1K/5K benchmark. It is up to 359.0 × and 13.9 × faster and 503.6 × and 68.7 × more energy-efficient than the baseline and optimized GPU implementation on Nvidia Titan RTX, respectively.
Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions

Increasing competition and loss of revenues force newsrooms to explore new digital solutions. The new solutions employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. The result is an emerging type of intelligent information system we have called the Journalistic Knowledge Platform (JKP). In this paper, we analyse for the first time knowledge graph-based JKPs in research and practice. We focus on their current state, challenges, opportunities and future directions. Our analysis is based on 14 platforms reported in research carried out in collaboration with news organisations and industry partners and our experiences with developing knowledge graph-based JKPs along with an industry partner. We found that: (a) the most central contribution of JKPs so far is to automate metadata annotation and monitoring tasks; (b) they also increasingly contribute to improving background information and content analysis, speeding-up newsroom workflows and providing newsworthy insights; (c) future JKPs need better mechanisms to extract information from textual and multimedia news items; (d) JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers’ demands.
Power profiling-based side-channel attacks on FPGA and Countermeasures: A survey

In recent years, there are many attacks revealed that can extract secret information from systems. These attacks are so powerful and serious threat to the security of systems. The side-channel attacks which use the power leakage of the cryptographic circuit during performing any operation to obtain a secret key are called power-based side-channel attacks. In this paper, we present a novel definition to explain these attacks, we present the difference between physical and remote power profiling methods and attacks. For our survey, we collect the more related and main information contained about power profiling-based attacks for purpose of a better literature survey. For the prevention of these attacks, we present the countermeasures based on masking and hiding techniques. We also present a brief discussion on attacks and their countermeasures.
An Ensemble Model for Software Defect Prediction

Software testing is one of the important ways to ensure the quality of software. It is found that testing cost more than 50% of overall project cost. Effective and efficient software testing utilizes the minimum resources of software. Therefore, it is important to construct the procedure which is not only able to perform the efficient testing but also minimizes the utilization of project resources. The goal of software testing is to find maximum defects in the software system. As world is continuously moving toward data driven approach for making important decision. Therefore, in this research paper we performed the machine learning analysis on the publicly available datasets and tried to achieve the maximum accuracy. The major focus of the paper is to apply different machine learning techniques on the datasets and find out which technique produce efficient result. Particularly, we proposed an ensemble learning models and perform comparative analysis among KNN, Decision tree, SVM and Na&#x00EF;ve Bayes on different datasets and it is demonstrated that performance of Ensemble method is more than other methods in term of accuracy, precision, recall and F1-score. The classification accuracy of ensemble model trained on CM1 is 98.56%, classification accuracy of ensemble model trained on KM2 is 98.18% similarly, the classification accuracy of ensemble learning model trained on PC1 is 99.27%. This reveals that ensemble learning is more efficient method for making the defect prediction as compared other techniques.
Understanding Self-Tracked Data from Bounded Situational Contexts

As smartphone and wearable tracking devices have grown in popularity, more individuals have begun collecting their own health data. While these data are often perceived as a persistent record of health and used to inform future behaviors, it is inevitable that some data are captured during a period of disruption or non-routine circumstances. If not appropriately contextualized, visualizations of these data can lead to missed opportunities in self-reflection, or worse, misinterpretation. To better understand how self-tracked data captured during non-routine circumstances are reflected upon after the disruption has ended, we interviewed women about how they might reflect on data from a recent pregnancy. We propose the concept of bounded situational context (BSC) to encapsulate how individuals define the boundaries of disruption within their data based on external and internal contexts. We discuss how self-tracking tools can be designed to align data visualizations with individuals’ perceived boundaries to aid in data interpretation.
SIFT: A Tool for Property Directed Symbolic Execution of Multithreaded Software

Analyzing multithreaded programs is notoriously hard due to the exponential number of thread interleavings. Although race detectors can help developers find and fix such bugs before the code is deployed, multithreaded code may still be buggy due to memory errors and assertion violations that are not due to race conditions. This paper presents a property directed symbolic execution of multithreaded code. Our approach, named SIFT, differs from previous work on detecting errors in multithreaded code by being property directed and by handling both memory safety and assertion checking that can be further customized by the user. SIFT can detect bugs that may or may not be due to data races, and works in an iterative way. In each step, it explores the state space using selective scheduling based on a set of interleaving points that have been inferred in the previous step. We have developed three partitioning strategies for improved effectiveness and performance. We have implemented SIFT on top of the KLEE symbolic execution engine and applied it to various real-world and academic benchmarks. SIFT could detect more vulnerabilities than a state-of-the-art memory vulnerability detector.
Support Vector Machine based Short Term Solar Power Prediction

As solar energy is one of the most abundant sources of renewable energy, there has been substantial fluctuations in the energy supply from PV systems when used with power grids. Solar power output depends on various factors such as cloud cover, temperature & relative humidity. By using the meteorological data to predict solar power generation, solar power becomes a reliable energy resource. This study investigates the use of Support Vector Regression for solar power prediction, which performs better than other machine learning algorithms. A variety of parameter tuning methods, such as Random search, Grid search and Tree based optimization tools are implemented to obtain a robust model that can identify the best model that will yield the least error when predicting the solar power generation.
PPA Based CNN Architecture Explorer

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly popular for implementing convolutional neural networks (CNNs) due to their low latency and high energy efficiency. In practice, a software designer first explores various CNN architectures in software to improve architecture's validation accuracy. Once an architecture is finalized, the designer must build a computation core on an FPGA for inference acceleration. The requirement of FPGA Performance, Power consumption, and Area (or resources) (PPA) is affected by many CNN model parameters, accelerator topology, and classification accuracy for a CNN implementation. However, the CNN mapping design space is enormous, and efficient mapping of CNN can quickly become a challenging task. Therefore, an exploration tool is essential for building a reconfigurable, fast, and efficient hardware accelerator. We have presented an integrated methodology for exploring FPGA-based CNN architectures by making tradeoffs between the performance, power, and area. This methodology becomes a mapping aid for software engineers who can evaluate the effect of CNN design on the implementation and performance of FPGA-based CNN accelerators.
Lectal contamination

Abstract This paper presents evidence from both corpora and agent-based simulation for the effect of lectal contamination. By doing so, it shows how agent-based simulation can be used as a complementary technique to corpus research in the study of language variation. Lectal contamination is an effect whereby the words that are typical of a language variety more often appear in a morphosyntactic variant typical of that same variety, even among language use from a different variety. This study looks at the Dutch partitive genitive construction, which exhibits variation between a “Netherlandic” variant with - s ending and a “Belgian” variant without - s ending. It is shown that the probability of the Belgian variant without - s increases among more “Belgian” words, in the language use of both Belgians and people from the Netherlands. Meanwhile, an agent-based simulation reveals the crucial theoretical preconditions that lead to this effect.
Determinacy of Real Conjunctive Queries. The Boolean Case

In their classical 1993 paper Chaudhuri and Vardi notice that some fundamental database theory results and techniques fail to survive when we try to see query answers as bags (multisets) of tuples rather than as sets of tuples.
Fine-grained bidirectional attentional generation and knowledge-assisted networks for cross-modal retrieval

Generally, most existing cross-modal retrieval methods only consider global or local semantic embeddings, lacking fine-grained dependencies between objects. At the same time, it is usually ignored that the mutual transformation between modalities also facilitates the embedding of modalities. Given these problems, we propose a method called BiKA (Bidirectional Knowledge-assisted embedding and Attention-based generation). The model uses a bidirectional graph convolutional neural network to establish dependencies between objects. In addition, it employs a bidirectional attention-based generative network to achieve the mutual transformation between modalities. Specifically, the knowledge graph is used for local matching to constrain the local expression of the modalities, in which the generative network is used for mutual transformation to constrain the global expression of the modalities. In addition, we also propose a new position relation embedding network to embed position relation information between objects. The experiments on two public datasets show that the performance of our method has been dramatically improved compared to many state-of-the-art models.
FPGA Implementation of Multibit LFSR as Key Generator for AES Encryption

The changing technology and urge for betterment of lifestyles. The innovation and creation is necessary part for acquiring the needed development. The main concerns in today is to provide an efficient system or devices which can withstand all the latest cyber threats and hacks. The latest test pattern generatores which can only provide one pattern at a single clock cycle. So it creates delay while testing a huge system for fault coverage. Reducing the time for testing a system has become a prime factor in case of BIST. SO in this paper a design is proposed which can produce multiple bits at a single clock cycle. This design can reduce the time consumed to generate random patterns. In addition as an application to implement an efficient cryptography system, The system should be tested to its maximum potential and it should be prevented from external interventions. In this paper the focus is on the implementation of a Multi-bit LFSR as a key generator for AES encryption. The Multi-bit LFSR is implemented in such a way that it is useful as a test-pattern generator in logic BIST to maximize the efficiency while testing the system.The influence of Logic BIST put forwards the significance of LFSR. The LFSR provides the patterns which are used to test the system. So, implementing an LFSR which can produce more efficient patterns will help in testing a system to its maximum potential and achieve good fault coverage for the circuit. The comparison of conventional LFSR is done with implemented Multi-bit LFSR to study the advantage. And as an application in the filed of cryptography, in this paper a encryption method is proposed with Multi-bit LFSR as a key generator for AES encryption standards. The implementation is carried out in Xilinx Vivado Tool. A spartan 7 FPGA board is used for the implementations to study the characteristics and utilization
Multi criteria analysis of multi-sources photovoltaic electrical structures in isolated sites

Multi-source photovoltaic system structures, play an important role in photovoltaic installations especially in isolated sites. However, requirements in terms of performance, power availability and lifetime have to meet the consumption requirements for their integration. In this context, authors propose a consistent approach to improve the choice of an adequate electrical topology of a multi-source photovoltaic system located in an isolated site. This approach is essentially performed by applying a multi-criteria analysis based on a comparison study including four criteria. Four different multi-source photovoltaic system structures are compared according to their life-time, power availability, installation complexity and cost. The comparison process is performed by applying the multi-criteria decision method PROMETHEE II.
The Shape of Social Media: Towards Addressing (Aesthetic) Design Power

The effects of social media on our society are heavily researched and discussed, but few insights about the role of aesthetic design therein have been gained to this date, despite research in related areas providing precedent: drawing from existing theory on persuasive design, nudge, dark patterns, and advertising, we suggest the term Aesthetic Design Power to conduct a preliminary critical analysis of the design of four influential social media platforms (Facebook, Twitter, Instagram, and TikTok) as a foundation for critical discussions about the aesthetic design of social media and its impact. We present salient design attributes and elements shared by these four platforms, speculate about the possible motivations and effects of these design decisions, and argue for an urgent need for further research into, and discussions about, the visual design and connected Aesthetic Design Power of social media.
Korean Semantic Role Labeling with Bidirectional Encoder Representations from Transformers and Simple Semantic Information

State-of-the-art semantic role labeling (SRL) performance has been achieved using neural network models by incorporating syntactic feature information such as dependency trees. In recent years, breakthroughs achieved using end-to-end neural network models have resulted in a state-of-the-art SRL performance even without syntactic features. With the advent of a language model called bidirectional encoder representations from transformers (BERT), another breakthrough was witnessed. Even though the semantic information of each word constituting a sentence is important in determining the meaning of a word, previous studies regarding the end-to-end neural network method did not utilize semantic information. In this study, we propose a BERT-based SRL model that uses simple semantic information without syntactic feature information. To obtain the latter, we used PropBank, which described the relational information between predicates and arguments. In addition, text-originated feature information obtained from the training text data was utilized. Our proposed model achieved state-of-the-art results on both Korean PropBank and CoNLL-2009 English benchmarks.
Exploration on Everyday Objects as an IoT Control Interface

As the user needs for IoT applications have grown, solutions for coordinating IoT devices in everyday living contexts are in high demand. This pictorial presents an explorative study and the design of a new IoT coordination approach that utilizes everyday objects as IoT control interfaces. We conducted a workshop study to explore the potential use cases and understand the opportunities and challenges of everyday objects as means for IoT coordination. Using the use cases from the workshop, we created a scenario movie to illustrate the user experience of the smart environment enabled by everyday objects. We also developed a functional prototype of the IoT coordination system with a wearable device that recognizes the usage of everyday objects. Based on the workshop and the design of two research artefacts, we discuss the implications for advancing the IoT ecology through the use of everyday objects and combining digital and non-digital artifacts.
Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual

DB-BERT is a database tuning tool that mines tuning hints from text documents, including the database manual. DB-BERT uses mined hints as a starting point for an iterative tuning approach, guided via reinforcement learning. This demonstration enables visitors to try out DB-BERT for tuning different database management systems, including Postgres and MySQL, on benchmarks such as TPC-C and TPC-H. Visitors can vary the input text to observe how mined hints influence DB-BERT's behavior. The demonstration interface allows tracing back configurations selected for trial runs to the text passages that motivated them. Finally, visitors may try to beat configurations proposed by DB-BERT with their own parameter settings. The code for this demo is publicly available at https://itrummer.github.io/dbbert/.
A Systematic Study on a Customer’s Next-Items Recommendation Techniques

A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.
Exploring the Effects of Self-Mockery to Improve Task-Oriented Chatbot’s Social Intelligence

An effective task-oriented chatbot should be able to exert a certain level of Social Intelligence (SI), the ability to emulate human social behaviors to reduce user frustration and dissatisfaction. However, few studies explored using humor, a common rhetorical device in human-human interactions, to improve chatbots' overall SI. To fill this gap, we proposed to apply self-mockery humor to a customer service chatbot in different interaction stages with users. We proposed a pipeline to create situated self-mockery for the chatbot and conducted a within-subject experiment (N=28) to compare it with a chatbot without self-mockery utterance. Results showed that the self-mockery chatbot was perceived as significantly funnier, more satisfactory, and delivering higher performance in two out of the five measured characteristics of SI with comparable performance in the rest. We further discussed how participants' individual factors might affect the perceived helpfulness of self-mockery on SI and concluded with design considerations.