Accurate detection of low-frequency DNA variants (below 1%) is essential in diverse biological and clinical contexts, yet remains fundamentally constrained by the high intrinsic error rates of next-generation sequencing technologies. Although unique molecular identifiers (UMIs) have significantly mitigated these errors by uniquely indexing original template molecules, their efficacy is compromised by UMI collisions and by artifacts introduced during polymerase chain reaction (PCR) amplification and sequencing, which collectively engender false-positive variant calls. Here, we present AFUMIC, an alignment-free UMI clustering framework that systematically addresses these limitations through collision-resilient UMI grouping and a consensus quality score (CQS)-guided strategy for high-fidelity consensus sequence generation. AFUMIC reduces singleton families, enhances clustering precision, and maximizes data retention, yielding 7.27-fold and 3.84-fold increases in single-strand consensus sequence and duplex consensus sequence output, respectively, compared to Du Novo. It further decreases the per-base error rate from $3.01 \times 10^{-4}$ to $2.10 \times 10^{-5}$ and raises the proportion of error-free positions from 45.27% to 99.85%, enabling confident detection of variants at variant allele frequencies as low as $1.0 \times 10^{-5}$. Notably, AFUMIC exhibits superior computational efficiency, rendering it well-suited for high-throughput analysis of UMI-tagged libraries in large-scale genomic studies. Collectively, AFUMIC represents an efficient methodology for ultrasensitive variant detection and establishes a broadly applicable and computationally efficient framework for error-corrected sequencing that can be readily deployed in both clinical diagnostics and large-scale genomic research.

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Due to limited data protection of many popular online services in some countries such as taxi hailing or takeouts ordering, many users encounter an increasing number of phone calls from strangers. This may aggravate the situation that criminals pretend to be delivery staff or taxi drivers, bringing threats to the society. Additionally, many people nowadays suffer from excessive digital marketing and fraud phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, and do not work for identification of multiple professions. We observed that web service requests issued from users' mobile phones which may show their Apps preferences, spatial and temporal patterns, and other profession related information. This offers us a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies or regulations (e.g. GDPR 71). Using appropriate statistical methods to eliminate private information and preserve personal characteristics, provides a way to identify mobile phone callers without privacy concern. In this paper, we exploit privacy-preserving mobile data to develop a model which can automatically identify the callers who are divided into four categories of users: normal users (other professions), taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters. The validation results over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City prove that the proposed model could achieve an accuracy of 75+%.

The rapid popularity of mobile devices especially smart phones has changed human life style greatly. In this paper, we examine the consumer behaviors on several e-commerce platforms based on a large-scale dataset of mobile internet access records for about 3.5 months from a major telecom operator in China, which covers 126,388 users from Shanghai. We provide a preliminary study on users' daily and periodic online shopping behaviors, as well as the influence of special online shopping events and gender factors. These findings may be exploited by e-commerce providers e.g., for developing personalized recommendation systems to improve their service quality and profit.

The proliferation of mobile devices especially smart phones brings remarkable opportunities for both industry and academia. In particular, the massive data generated from users» usage logs provide the possibilities for stakeholders to know better about consumer behaviors with the aid of data mining. In this paper, we examine the consumer behaviors across multiple platforms based on a large-scale mobile Internet dataset from a major telecom operator, which covers 9.8 million users from two regions among which 1.4 million users have visited e-commerce platforms within one week of our study. We make several interesting observations and examine users» cultural differences from different regions. Our analysis shows among the multiple e-commerce platforms available, most mobile users are loyal to their favorable sites; people (60%) tend to make quick decisions to buy something online, which usually takes less than half an hour. Furthermore, we find that people in residential areas are much easier to perform purchases than in business districts and purchases take place during non-work time. Meanwhile, people with medium socioeconomic status like browsing and purchasing on e-commerce platforms, while people with high and low socioeconomic status are much easier to conduct purchases online. We also show the predictability of cross-platform shopping behaviors with extensive experiments on the basis of our observed data. Our discoveries could be a good guide for e-commerce future strategy making.

This paper proposes a novel scheme for RFID anti-counterfeiting by applying bisectional multivariate quadratic equations (BMQE) system into an RF tag data encryption. In the key generation process, arbitrarily choose two matrix sets (denoted as A and B) and a base Rab such that [AB] = λRABT, and generate 2n BMQ polynomials (denoted as p) over finite field Fq. Therefore, (Fq, p) is taken as a public key and (A, B, λ) as a private key. In the encryption process, the EPC code is hashed into a message digest dm. Then dmis padded to d'mwhich is a non-zero 2n×2n matrix over Fq. With (A, B, λ) and d'm, Smis formed as an n-vector over F2. Unlike the existing anti-counterfeit scheme, the one we proposed is based on quantum cryptography, thus it is robust enough to resist the existing attacks and has high security.

High-level representation of temporary traffic states is very crucial for intelligent traffic management applications, but a challenging task due to the complexity of the traffic states. In this paper, we present theoretically a high-level representation of temporary regional traffic states using Hasse graph and temporal change map under a grid-based site model. We first model the region within the monitoring range of the camera as an uncovered house and express it with a set of 4-tuples. Then we formulate a partial ordered relation with grid coordinates of the extracted objects in a specific site from which a Hasse graph is yielded. On the timeline, difference between two consecutive Hasse graphs makes a variable temporal change map. All temporary traffic states can be categorized into two types: one is the motion of a single object, represented simply with the Hasse graph of its location and the temporal change map; the other is the set of grouped patterns of all the extracted object, or the Hasse graph of the neighboring objects. With this representation and the fuzzy concept of neighborhood, key traffic states like collision and secure distance can be distinguished and determined using appropriate motion prediction based on the previous measurement to manipulate the undesirable cases on time.