Query Length Research Articles

Online and Offline Evaluation in Search Clarification

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement. Contrary to the current understanding that offline evaluations fall short of supporting online evaluations, we indicate that when identifying the most engaging clarification questions from the user’s perspective, online and offline evaluations correspond with each other. We show that the query length does not influence the relationship between online and offline evaluations, and reducing uncertainty in online evaluation strengthens this relationship. We illustrate that an engaging clarification needs to excel from multiple perspectives, and SERP quality and characteristics of the clarification are equally important. We also investigate if human labels can enhance the performance of Large Language Models (LLMs) and Learning-to-Rank (LTR) models in identifying the most engaging clarification questions from the user’s perspective by incorporating offline evaluations as input features. Our results indicate that LTR models do not perform better than individual offline labels. However, GPT, an LLM, emerges as the standout performer, surpassing all LTR models and offline labels.

No evidence that mutations in SARS-CoV-2 variants of concern derive from homologous fragments in gut microbiota.

Understanding the origin and evolution of mutations in SARS-CoV-2 variants of concern (VOCs) is a critical area of research. B. Cao, X. Wang, W. Yin, Z. Gao, and B. Xia (mBio 15:e03187-23, 2024, https://doi.org/10.1128/mbio.03187-23) proposed that these mutations originated from bacterial sequences incorporated into the viral genome through stochastic template-switching by the viral RNA-dependent RNA polymerase (RdRp). Their analysis suggested that 62% of the viral mutation fragments (VMFs) in key SARS-CoV-2 proteins were identical to bacterial protein sequences. Given the implications of this finding, we re-examined the methods employed and argue that they resulted in false-positive findings. Specifically, the short query length of VMFs, seven amino acids, leads to spurious matches in large protein databases, as indicated by high BLAST Expect values. Furthermore, we analyzed the nucleotide sequence of VMFs, revealing no unique homology between SARS-CoV-2 and bacterial sequences. Consequently, the evidence does not support the hypothesis that bacterial sequences contribute to the evolution of SARS-CoV-2 VOCs. Instead, the emergence of these variants is more plausibly attributed to factors intrinsic to viral replication and evolution, such as the error-prone nature of RdRp, intrahost diversity, and recombination of related viral sublineages.

A Long Short Term Memory Model for Character-based Analysis of DNS Tunneling Detection

DNS tunneling is the attempt to create a hidden tunnel through a domain name service. Such a tunnel would jeopardize the targeted network and open the door for illegal access, control, and data exfiltration. The information security research community showed the variety of techniques that have been proposed to detect the tunnel. The majority of these efforts were relying on machine learning techniques where features of tunneling are considered such as length of DNS query, size, and entropy of the query. However, an additional analysis of the lexical information of the DNS query has been depicted recently and showed remarkable performance. This paper aims to examine the role of Long Short Term Memory (LSTM) model in terms of DNS lexical analysis. Two benchmark datasets related to DNS have been used. In addition, a character mapping mechanism has been used to replace every possible character with an integer number. Consequentially, the mapped representation has been fed into an LSTM model for DNS tunneling detection. Results showed that the proposed method was able to obtain a weighted average F1-score of 98% for both datasets respectively. Such results are competitive in the context of the state of the art and demonstrate the efficacy of the lexical analysis within the DNS tunneling detection task.

Graphite: painting genomes using a colored de Bruijn graph.

The recent growth of microbial sequence data allows comparisons at unprecedented scales, enabling the tracking of strains, mobile genetic elements, or genes. Querying a genome against a large reference database can easily yield thousands of matches that are tedious to interpret and pose computational challenges. We developed Graphite that uses a colored de Bruijn graph (cDBG) to paint query genomes, selecting the local best matches along the full query length. By focusing on the best genomic match of each query region, Graphite reduces the number of matches while providing the most promising leads for sequence tracking or genomic forensics. When applied to hundreds of Campylobacter genomes we found extensive gene sharing, including a previously undetected C. coli plasmid that matched a C. jejuni chromosome. Together, genome painting using cDBGs as enabled by Graphite, can reveal new biological phenomena by mitigating computational hurdles.

Indexing and searching petabase-scale nucleotide resources.

Searching vast and rapidly growing nucleotide content in resources, such as runs in the Sequence Read Archive and assemblies for whole-genome shotgun sequencing projects in GenBank, is currently impractical for most researchers. Here we present Pebblescout, a tool that navigates such content by providing indexing and search capabilities. Indexing uses dense sampling of the sequences in the resource. Search finds subjects (runs or assemblies) that have short sequence matches to a user query, with well-defined guarantees and ranks them using informativeness of the matches. We illustrate the functionality of Pebblescout by creating eight databases that index over 3.7 petabases. The web service of Pebblescout can be reached at https://pebblescout.ncbi.nlm.nih.gov . We show that for a wide range of query lengths, Pebblescout provides a data-driven way for finding relevant subsets of large nucleotide resources, reducing the effort for downstream analysis substantially. We also show that Pebblescout results compare favorably to MetaGraph and Sourmash.

A full-length 18S ribosomal DNA metabarcoding approach for determining protist community diversity using Nanopore sequencing.

Protist diversity studies are frequently conducted using DNA metabarcoding methods. Currently, most studies have utilized short read sequences to assess protist diversity. One limitation of using short read sequences is the low resolution of the markers. For better taxonomic resolution longer sequences of the 18S rDNA are required because the full-length has both conserved and hypervariable regions. In this study, a new primer pair combination was used to amplify the full-length 18S rDNA and its efficacy was validated with a test community and then validated with field samples. Full-length sequences obtained with the Nanopore MinION for protist diversity from field samples were compared with Illumina MiSeq V4 and V8-V9 short reads. Sequences generated from the high-throughput sequencers are Amplicon Sequence Variants (ASVs). Metabarcoding results show high congruency among the long reads and short reads in taxonomic annotation at the major taxonomic group level; however, not all taxa could be successfully detected from sequences. Based on the criteria of ≥95% similarity and ≥1000 bp query length, 298 genera were identified by all markers in the field samples, 250 (84%) were detected by 18S, while only 226 (76%) by V4 and 213 (71%) by V8-V9. Of the total 85 dinoflagellate genera observed, 19 genera were not defined by 18S dinoflagellate ASVs compared to only three among the total 52 diatom genera. The discrepancy in this resolution is due to the lack of taxonomically available 18S reference sequences in particular for dinoflagellates. Overall, this preliminary investigation demonstrates that application of the full-length 18S rDNA approach can be successful in field studies.

$\textsf{LightMAC}$: Fork it and make it faster

In FSE'16, Lyukx et al. have proposed $\textsf{LightMAC}$ that achieves a query length independent security of $ O(q^2/2^n) $ when instantiated with two independently keyed block ciphers. This high security comes at the cost of reducing the data injection rate to $ (n-s) $ bits per primitive invocation and restricting the query length up to $ (n-s)2^s $ bits, for a fixed counter size $ s $. Recently in ASIACRYPT'21, Chattopadhyay et al. have shown that $\textsf{LightMAC}$ achieves the same security even when it is instantiated with a single keyed block cipher. However, it limits the length of a message to $ (n-s) \min\{2^{n/4}, 2^s\} $ bits for a fixed counter size $ s $. In this paper, we propose $\textsf{LightFORK}$, a forkcipher variant of $\textsf{LightMAC}$ that achieves an improved query length independent security bound of the order of $ O(q^2/2^{n+s}) $, maintaining full $ n $ bit message injection per primitive call, where $ n $ and $ s $ denote the block size and tweak size of the forkcipher respectively. The maximum message length is also increased to $ n2^{n/6+s/2} $ bits. Our security proof is based on a new technique called resetting with delayed sampling, which is an extension of the reset-sampling technique of Chattopadhyay et al.

Towards the universal defense for query-based audio adversarial attacks on speech recognition system

Recently, studies show that deep learning-based automatic speech recognition (ASR) systems are vulnerable to adversarial examples (AEs), which add a small amount of noise to the original audio examples. These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices. The existing defense methods are either limited in application or only defend on results, but not on process. In this work, we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process. The insight of this method is based on the observation: many existing audio AE attacks utilize query-based methods, which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process. Inspired by this observation, We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query. Thus, we can identify when a sequence of queries appears to be suspectable to generate audio AEs. Through extensive evaluation on four state-of-the-art audio AE attacks, we demonstrate that on average our defense identify the adversary’s intent with over 90% accuracy. With careful regard for robustness evaluations, we also analyze our proposed defense and its strength to withstand two adaptive attacks. Finally, our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.

A Range Query Method for Data Access Pattern Protection Based on Uniform Access Frequency Distribution

Data encryption is necessary to keep user information secure and private on the cloud. However, adversaries can still learn valuable information about the encrypted data by observing data access patterns. To solve this issue, Oblivious RAMs (ORAMs) are proposed to hide access patterns. However, ORAMs are expensive and not suitable for deployment in a large database. In this work, we propose a range query algorithm while providing data access pattern protection based on uniform access frequency. In the preprocessing, multiple key-value pairs in the database are grouped and stored in each storage module, and we make copies for frequently accessed key-value pairs and also add some dummy key-value pairs on each storage module. In the online query processing, according to the range query length of the received query access request, we visit the specific storage module for the query and obtain the query result. Based on the techniques above, our method makes the uniform distribution of access frequency of data blocks in the database and achieves a security guarantee as strong as the state-of-the-art method. Compared with data queries that do not provide data access pattern protection, the ratio of network communication overhead is constant rather than logarithmic in ORAMs.

Bioremediation Tofu Liquid Waste Based on Chemical Oxygen Demand (COD) Parameters

Background and Objective: The majority of liquid waste generated by the tofu industry in Semarang exceeds the recommended limit. This condition disturbs the ecology of the surrounding environment. so a solution is needed to manage it. The use of bacteria in bioremediation technology has been proven to reduce pollutants produced by the tofu industry. The purpose of this study was to identify specific bacteria and test their ability to bioremediate pollutants from tofu wastewater using the Chemical Oxygen Demand (COD) parameter.
 Methodology: Methodology: The study was exploratory and experimental in nature, with the variable ability of degradative bacteria degrading tofu industrial wastewater based on COD parameters. Bacterial identification was performed using the 16S-rRNA Gene Analysis method, genomic DNA extraction with the Quick-DNA Bacterial Miniprep Kit, PCR amplification, and bi-directional sequencing. 
 Results: The results of the identification of the selected bacteria show that they are molecules of the type nucleic acid with a query length of 1394. with a lineage report and a 93% phylogenetic tree as Bacillus subtilis, Bioremediation test shows that tofu waste can be degraded. The degradation percentage is 17%.
 Conclusion: Bacillus subtillis, a bacterium that degrades tofu industrial wastewater, was found as an indigenous bacteria.

Complete Genome Sequences of Four Strains ofErwiniatracheiphila: A Resource for Studying aBacterial Plant Pathogen with a Highly Complex Genome.

Complete Genome Sequences of Four Strains ofErwiniatracheiphila: A Resource for Studying aBacterial Plant Pathogen with a Highly Complex Genome.

Generalized framework for Group Testing: Queries, feedbacks and adversaries

In the Group Testing problem, the objective is to learn a subset K of some much larger domain N, using the shortest-possible sequence of queries Q. A feedback to a query provides some information about the intersection between the query and subset K. Several specific feedbacks have been studied in the literature, often proving different formulas for the estimate of the query complexity of the problem, defined as the shortest length of queries' sequence solving Group Testing problem with specific feedback. In this paper we study what are the properties of the feedback that influence the query complexity of Group Testing and what is their measurable impact. We propose a generic framework that covers a vast majority of relevant settings considered in the literature, which depends on two fundamental parameters of the feedback: input capacity α and output expressiveness β. They upper bound the logarithm of the size of the feedback function domain and image, respectively. To justify the value of the framework, we prove upper bounds on query complexity of non-adaptive, deterministic Group Testing under some “efficient” feedbacks, for minimum, maximum and general expressiveness, and complement them with a lower bound on all feedbacks with given parameters α,β. Our upper bounds also hold if the feedback function could get an input twisted by a malicious adversary, in case the intersection of a query and the hidden set is bigger than the feedback capacity α. We also show that slight change in the feedback function may result in substantial worsening of the query complexity. Additionally, we analyze explicitly constructed randomized counterparts of the deterministic results. Our results provide some insights to what are the most useful bits of information an output-restricted feedback could provide, and open a number of challenging research directions.

A hybrid neuro-fuzzy technique to overcome clustering approach issues in big data

MapReduce is regarded as one of the major enabling methods in the big data community for handling the ever-increasing demands on computer resources imposed by enormous datasets. Currently, Google's MapReduce or its open-source equivalent Hadoop are well-known solutions for these types of applications. Several techniques are developed based on Hadoop such as apriori(M), HFUPM, apriori, EFUPM. The conventional algorithms of clustering schemes suffer from many challenging issues as clustering error, and computational complexity. To overcome the challenges of existing techniques we develop a novel approach to data pre-processing to extract the significant information. After pre-processing the data is processed applying neuro fuzzy-based scheduler to obtain semantic relationship between user queries. The fuzzy logic considers query time, query length, query expiry, total queries, CPU usages, and task activity. The proposed approach is implemented using a Hadoop platform. The comparative study shows a significant improvement in MapReduce performance for huge dataset.

The Design and Implementation of an Improved Lightweight BLASTP on CUDA GPU

In the field of computational biology, sequence alignment is a very important methodology. BLAST is a very common tool for performing sequence alignment in bioinformatics provided by National Center for Biotechnology Information (NCBI) in the USA. The BLAST server receives tens of thousands of queries every day on average. Among the procedures of BLAST, the hit detection process whose core architecture is a lookup table is the most time-consuming. In the latest work, a lightweight BLASTP on CUDA GPU with a hybrid query-index table was proposed for servicing the sequence query length shorter than 512, which effectively improved the query efficiency. According to the reported protein sequence length distribution, about 90% of sequences are equal to or smaller than 1024. In this paper, we propose an improved lightweight BLASTP to speed up the hit detection time for longer query sequences. The largest sequence is enlarged from 512 to 1024. As a result, one more bit is required to encode each sequence position. To meet the requirement, an extended hybrid query-index table (EHQIT) is proposed to accommodate three sequence positions in a four-byte table entry, making only one memory access sufficient to retrieve all the position information as long as the number of hits is equal to or smaller than three. Moreover, if there are more than three hits for a possible word, all the position information will be stored in contiguous table entries, which eliminates branch divergence and reduces memory space for pointers to overflow buffer. A square symmetric scoring matrix, Blosum62, is used to determine the relative score made by matching two characters in a sequence alignment. The experimental results show that for queries shorter than 512 our improved lightweight BLASTP outperforms the original lightweight BLASTP with speedups of 1.2 on average. When the number of hit overflows increases, the speedup can be as high as two. For queries shorter than 1024, our improved lightweight BLASTP can provide speedups ranging from 1.56 to 3.08 over the CUDA-BLAST. In short, the improved lightweight BLASTP can replace the original one because it can support a longer query sequence and provide better performance.

Abstract 10698: Towards a Dialogue Agent to Assist African American and Hispanic/Latino Heart Failure Patients with Dietary Restrictions

Background: Restricting dietary sodium is a common recommendation to address symptoms in heart failure (HF) patients. However, using the information provided on nutrition labels can be complex, leading to lapses in adherence. This problem is compounded by insufficient information on ethnic preferences in dietary habits. Based on our previous findings we developed a prototype dialogue agent (HF-DA) to assist African American (AA) and Hispanic Latino (H/L) patients with their HF self-care needs, including understanding nutrition labels and providing practical information per FDA recommendations. Objective: Compare HF-DA against existing technology in the public domain in assisting AA and H/L HF patients to make healthy food choices. Methods: We conducted a review of existing technology in two ways. 1: Google search engine: We measured the number of actions (typing, scrolling, clicking) required to find a source for a food’s sodium content matching the U.S. Department of Agriculture’s recommendations and used a calculator to understand the total sodium in a desired serving size. We identified relevant search terms by analyzing the commonly used words in the transcripts from our HF patient education sessions with their health educators (18 AA and 2 H/L) and 1:1 interviews with HF patients (3 AA and 1 H/L). 2: Google Play Store: We downloaded the top 15 apps using the search term “heart failure” and explored their functionality. Results: Google’s search engine required users to perform 1 action to identify the sodium content and percent daily value but then needed 6-8 actions (dependent on query length) to calculate sodium content of total serving size. Our HF-DA provides this information in 4 actions. Of the 15 apps available in the Play Store, none had any features for understanding nutrition labels. Conclusion: Our preliminary analysis revealed there are very few culturally sensitive technological resources available to assist HF patients in understanding nutrition labels. These resources require a patient to understand nutrition labels, perform arithmetic, and recall dietary restrictions. HF-DA is capable of promoting healthy eating habits by aiding HF patients' decision-making process by providing reliable information in fewer steps.

On Length Independent Security Bounds for the PMAC Family

At FSE 2017, Gaži et al. demonstrated a pseudorandom function (PRF) distinguisher (Gaži et al., ToSC 2016(2)) on PMAC with Ω(lq2/2n) advantage, where q, l, and n, denote the number of queries, maximum permissible query length (in terms of n-bit blocks), and block size of the underlying block cipher. This, in combination with the upper bounds of Ο(lq2/2n) (Minematsu and Matsushima, FSE 2007) and Ο(qσ/2n) (Nandi and Mandal, J. Mathematical Cryptology 2008(2)), resolved the long-standing problem of exact security of PMAC. Gaži et al. also showed that the dependency on l can be dropped (i.e. O(q2/2n) bound up to l ≤ 2n/2) for a simplified version of PMAC, called sPMAC, by replacing the Gray code-based masking in PMAC with any 4-wise independent universal hash-based masking. Recently, Naito proposed another variant of PMAC with two powering-up maskings (Naito, ToSC 2019(2)) that achieves l-free bound of O(q2/2n), provided l ≤ 2n/2. In this work, we first identify a flaw in the analysis of Naito’s PMAC variant that invalidates the security proof. Apparently, the flaw is not easy to fix under the existing proof setup. We then formulate an equivalent problem which must be solved in order to achieve l-free security bounds for this variant. Second, we show that sPMAC achieves O(q2/2n) bound for a weaker notion of universality as compared to the earlier condition of 4-wise independence. Third, we analyze the security of PMAC1 (a popular variant of PMAC) with a simple modification in the linear combination of block cipher outputs. We show that this simple modification of PMAC1 has tight security O(q2/2n) provided l ≤ 2n/4. Even if l < 2n/4, we still achieve same tight bound as long as total number of blocks in all queries is less than 22n/3.

RDFM: An alternative approach for representing, storing, and maintaining meta-knowledge in web of data

The Web of Data needs additional information, i.e., meta-knowledge to ensure quality and build the trust in data. For representing meta-knowledge, there exist various approaches in the literature, for example, RDFt, tRDF, RDF Reification, Singleton Property and Named Graph. There are various issues associated with these approaches in representing the meta-knowledge, for example, the increasing graph size, additional statement generation, the representation of multi-dimensional and/or nested meta-knowledge, among others. In this work, we propose an approach called RDFM to represent, store, and manage meta-knowledge. RDFM integrates attributes to the predicate to represent nested and/or multi-dimensional meta-knowledge with lesser statements generation. A query language called SPARQLM is developed to extract RDFM data. The study analyzes the performance of RDFM in terms of the number of edges, number of statements generation, data redundancy, storage, query response time, required query length, representation of meta-knowledge in nested and different dimensions. The results show that the RDFM model performs significantly and gives advantage over storage management and graph data management.

Comparative Analysis of Information Retrieval Models on Quran Dataset in Cross-Language Information Retrieval Systems

English is an international language used for communication worldwide but still many cannot read, write, understand, or communicate in English. On the other hand, the World Wide Web has unlimited resources of information in different languages which English native find challenging to understand. To avoid such barriers, Cross-Language Information Retrieval (CLIR) systems are proposed, which refers to document retrieval tasks across different languages. This work focuses on the performance evaluation of different Information Retrieval (IR) models in CLIR system using Quran dataset. Furthermore, this work also investigated the length of query and query expansion models for effective retrieval. The results show that different length of queries has an impact on the performance of the retrieval methods in terms of effectiveness. Hence, after comprehensive experiments, an appropriate length of query for Arabic CLIR system is suggested along with the best query expansion and retrieval model.

Examination of Effects of Time Constraint and Task Type on Users' Query Typing Behaviors

Contextual factors have been found to be an important factor in information searching behaviors, however, little attention has been paid to the influence of contextual factors on users' query typing behaviors. This study aims to explore the influence of two different contextual factors (with or without time constraint and two kinds of task type) on users' query typing behaviors. We recruited 40 college students and conducted a user experiment, in which each participant completed two types of search tasks (Fact Finding and Information Understanding) in two different time conditions. The results show that time constraint encourages users to increase their query typing speed. Furthermore, the task type affects query length and rate of keystroke errors.

Database security: combining neural networks and classification approach

In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take the advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to a recurrent neural network (RNN) trained by backpropagation through time (BPTT) algorithm. Then, two coefficients of the rule are evaluated. The rule is used to interpret RNN output. In the testing phase RNN with the rule is examined against attacks and legal data to find out how evaluated rule affects efficiency of detecting attacks. All experiments were conducted on Jordan network. Experimental results show the relationship between the rule and a length of SQL queries.