Use case descriptions describe features consisting of multiple concepts with following a procedural flow. Because existing feature location techniques lack a relation between concepts in such features, it is difficult to identify the concepts in the source code with high accuracy. This paper presents a technique to locate concepts in a feature described in a use case description consisting of multiple use case steps using dependency between them. We regard each use case step as a description of a concept and apply an existing concept location technique to the descriptions of concepts and obtain lists of modules. Also, three types of dependencies: time, call, and data dependencies among use case steps are extracted based on their textual description. Modules in the obtained lists failing to match the dependency between concepts are filtered out. Thus, we can obtain more precise lists of modules. We have applied our technique to use case descriptions in a benchmark. Results show that our technique outperformed baseline setting without applying the filtering.

Text classification is a fundamental task in natural language processing, which finds extensive applications in various domains, such as spam detection and sentiment analysis. Syntactic information can be effectively utilized to improve the performance of neural network models in understanding the semantics of text. The Chinese text exhibits a high degree of syntactic complexity, with individual words often possessing multiple parts of speech. In this paper, we propose BRsyn-caps, a capsule network-based Chinese text classification model that leverages both Bert and dependency syntax. Our proposed approach integrates semantic information through Bert pre-training model for obtaining word representations, extracts contextual information through Long Short-term memory neural network (LSTM), encodes syntactic dependency trees through graph attention neural network, and utilizes capsule network to effectively integrate features for text classification. Additionally, we propose a character-level syntactic dependency tree adjacency matrix construction algorithm, which can introduce syntactic information into character-level representation. Experiments on five datasets demonstrate that BRsyn-caps can effectively integrate semantic, sequential, and syntactic information in text, proving the effectiveness of our proposed method for Chinese text classification.

As the Internet has become prevalent, the popularity of net media has been growing, to a point that it has taken over conventional mass media. However, TWtrends, the Twitter trends visualization system operated by our research team since 2019, indicates that many topics on TV programs frequently appear on Twitter trendlines. This study investigates the relationship between Twitter and TV programs by collecting information on Twitter trends and TV programs simultaneously. Although this study provides a rough estimation of the volume of tweets that mention TV programs, the results show that several tweets mention TV programs at a constant rate, which tends to increase on the weekend. This tendency of TV-related tweets stems from the audience rating survey results. Considering the study outcome, and the fact that many TV programs introduce topics popular in social media, implies codependency between Internet media (social media) and mass media.

Temperature-independent zero-zero-birefringence polymer (TIZZBP), which exhibits very small birefringence over the wide temperature range, is required to realize real-color images for displays, particularly vehicle-mounted displays. Previously, a TIZZBP was synthesized, but they did not put into practical use because of their too complex composition and low mechanical strength. In this paper, we propose a practical TIZZBP that has high heat resistance, high transparency and sufficient mechanical strength, using a simple binary copolymerization system. Our proposed novel polymer exhibits very low photoelastic birefringence and very low orientational birefringence. Both types of birefringence of this TIZZBP satisfy the negligible levels for displays, which are defined as follows: the absolute values of photoelastic coefficient and intrinsic birefringence are less than 1 ×10-12 Pa-1 and 1 ×10-3, respectively. In addition, temperature dependency of orientational birefringence was very low. Orientational birefringence satisfies the negligible level all over the temperature range from around -40°C to 85°C. This temperature range is important because it is the operational temperature range for vehicle-mounted display. Furthermore, our proposed novel TIZZBP showed high heat resistance, high transparency and sufficient mechanical strength. The glass transition temperature was 194°C. The total light transmittance and the haze value is more than 91% and less than 1%, respectively. The tensile strength of non-oriented films was 35 ~ 50 MPa. These results suggest our proposed novel TIZZBP has high practicality in addition to very low birefringence. Therefore, this TIZZBP film will be very useful for various displays including vehicle-mounted displays and flexible displays.

This paper discusses a static analysis problem, called absolute consistency problem, for relational schema mappings. A given schema mapping is said to be absolutely consistent if every source instance has a corresponding target instance. Absolute consistency is an important property because it guarantees that data exchange never fails for any source instance. Originally, for XML schema mappings, the absolute consistency problem was defined and its complexity was investigated by Amano et al. However, as far as the authors know, there are no known results for relational schema mappings. In this paper, we focus on relational schema mappings such that both the source and the target schemas have functional dependencies, under the assumption that mapping rules are defined by constant-free tuple-generating dependencies. In this setting, we show that the absolute consistency problem is in coNP. We also show that it is solvable in polynomial time if the tuple-generating dependencies are full and the size of the left-hand side of each functional dependency is bounded by some constant. Finally, we show that the absolute consistency problem is coNP-hard even if the source schema has no functional dependency and the target schema has only one; or each of the source and the target schemas has only one functional dependency such that the size of the left-hand side of the functional dependency is at most two.

Logging is a practical and useful way of diagnosing failures in software systems. The logged events are crucially important to learning what happened during a failure. If key events are not logged, it is almost impossible to track error propagations in the diagnosis. Tracking an error propagation becomes utterly complicated if inter-thread data dependency is involved. An inter-thread data dependency arises when one thread accesses to share data corrupted by another thread. Since the erroneous state propagates from a buggy thread to a failing thread through the corrupt shared data, the root cause cannot be tracked back solely by investigating the failing thread. This paper presents the design and implementation of K9, a tool that inserts logging code automatically to trace inter-thread data dependencies. K9 is designed to be “practical”; it scales to one million lines of code in C, causes negligible runtime overheads, and provides clues to tracking inter-thread dependencies in real-world bugs. To scale to one million lines of code, K9 ditches rigorous static analysis of pointers to detect code locations where inter-thread data dependency can occur. Instead, K9 takes the best-effort approach and finds out “most” of those code locations by making use of coding conventions. This paper demonstrates that K9 is applicable to Linux and captures relevant code locations, in spite of the best-effort approach, enough to provide useful clues to root causes in real-world bugs, including a previously unknown bug in Linux. The paper also shows K9 runtime overhead is negligible. K9 incurs 1.25% throughput degradation and 0.18% CPU usage increase, on average, in our evaluation.

As Internet-connected service is emerged, there has been a need for use cases where a lightweight cryptographic primitive meets both of a constrained hardware implementation requirement and a constrained embedded software requirement. One of the examples of these use cases is the PKES (Passive Keyless Entry and Start) system in an automotive domain. From the perspective on these use cases, one interesting direction is to investigate how small the memory (RAM/ROM) requirement of ARM-implementations of hardware-oriented stream ciphers can be. In this paper, we propose implementation techniques for memory-optimized implementations of lightweight hardware-oriented stream ciphers including Grain-128a specified in ISO/IEC 29167-13 for RFID protocols. Our techniques include data-dependency analysis to take a close look at how and in which timing certain variables are updated and also the way taking into account the structure of registers on the target micro-controller. In order to minimize RAM size, we reduce the number of general purpose registers for computation of Grain-128a's update and pre-output values. We present results of our memory-optimized implementations of Grain-128a, one of which requires 84 RAM bytes on ARM Cortex-M3.

Word order is one of the most significant differences between the Chinese and Vietnamese. In the phrase-based statistical machine translation, the reordering model will learn reordering rules from bilingual corpora. If the bilingual corpora are large and good enough, the reordering rules are exact and coverable. However, Chinese-Vietnamese is a low-resource language pair, the extraction of reordering rules is limited. This leads to the quality of reordering in Chinese-Vietnamese machine translation is not high. In this paper, we have combined Chinese dependency relation and Chinese-Vietnamese word alignment results in order to pre-order Chinese word order to be suitable to Vietnamese one. The experimental results show that our methodology has improved the machine translation performance compared to the translation system using only the reordering models of phrase-based statistical machine translation.

We have previously introduced the static dependency pair method that proves termination by analyzing the static recursive structure of various extensions of term rewriting systems for handling higher-order functions. The key is to succeed with the formalization of recursive structures based on the notion of strong computability, which is introduced for the termination of typed λ-calculi. To bring the static dependency pair method close to existing functional programs, we also extend the method to term rewriting models in which functional abstractions with patterns are permitted. Since the static dependency pair method is not sound in general, we formulate a class; namely, accessibility, in which the method works well. The static dependency pair method is a very natural reasoning; therefore, our extension differs only slightly from previous results. On the other hand, a soundness proof is dramatically difficult.

This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or N-gram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.

In this paper, we propose a deep learning based model for classifying speech-acts using a convolutional neural network (CNN). The model uses some bigram features including parts-of-speech (POS) tags and dependency-relation bigrams, which represent syntactic structural information in utterances. Previous classification approaches using CNN have commonly exploited word embeddings using morpheme unigrams. However, the proposed model first extracts two different bigram features that well reflect the syntactic structure of utterances and then represents them as a vector representation using a word embedding technique. As a result, the proposed model using bigram embeddings achieves an accuracy of 89.05%. Furthermore, the accuracy of this model is relatively 2.8% higher than that of competitive models in previous studies.

The translation based language model (TRLM) is state-of-the-art method to solve the lexical gap problem of the question retrieval in the community-based question answering (cQA). Some researchers tried to find methods for solving the lexical gap and improving the TRLM. In this paper, we propose a new dependency based model (DM) for the question retrieval. We explore how to utilize the results of a dependency parser for cQA. Dependency bigrams are extracted from the dependency parser and the language model is transformed using the dependency bigrams as bigram features. As a result, we obtain the significant improved performances when TRLM and DM approaches are effectively combined.

We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time.

Software architecture recovery techniques are often adopted to derive a module view of software from its source code in case software architecture documents are unavailable or outdated. The module view is one of the most important perspectives of software architecture. In this paper, we propose a novel approach to derive a module view by adaptively integrating structural dependency and textual similarity. Our approach utilizes Newman modularity and Shannon information entropy to determine the appropriate weights of the dependencies during the integration. We apply our approach to various open-source projects and show the experimental results validating the effectiveness of the approach.

Compared to the traditional functional dependency (FD), the extended conditional functional dependency (CFD) has shown greater potential for detecting and repairing inconsistent data. CFDMiner is a widely used algorithm for mining constant-CFDs. But the search space of CFDMiner is too large, and there is still room for efficiency improvement. In this paper, an efficient pruning strategy is proposed to optimize the algorithm by reducing the search space. Both theoretical analysis and experiments have proved the optimized algorithm can produce the consistent results as the original CFDMiner.

Recent work on joint word segmentation, POS (Part Of Speech) tagging, and dependency parsing in Chinese has two key problems: the first is that word segmentation based on character and dependency parsing based on word were not combined well in the transition-based framework, and the second is that the joint model suffers from the insufficiency of annotated corpus. In order to resolve the first problem, we propose to transform the traditional word-based dependency tree into character-based dependency tree by using the internal structure of words and then propose a novel character-level joint model for the three tasks. In order to resolve the second problem, we propose a novel semi-supervised joint model for exploiting n-gram feature and dependency subtree feature from partially-annotated corpus. Experimental results on the Chinese Treebank show that our joint model achieved 98.31%, 94.84% and 81.71% for Chinese word segmentation, POS tagging, and dependency parsing, respectively. Our model outperforms the pipeline model of the three tasks by 0.92%, 1.77% and 3.95%, respectively. Particularly, the F1 value of word segmentation and POS tagging achieved the best result compared with those reported until now.

This paper describes a method of identifying nonlocal dependencies in incremental parsing. Our incremental parser inserts empty elements at arbitrary positions to generate partial parse trees including empty elements. To identify the correspondence between empty elements and their fillers, our method adapts a hybrid approach: slash feature annotation and heuristic rules. This decreases local ambiguity in incremental parsing and improves the accuracy of our parser.

Feature Selection (FS) is widely used to resolve the problem of selecting a subset of information-rich features; Fuzzy-Rough QuickReduct (FRQR) is one of the most successful FS methods. This paper presents two variants of the FRQR algorithm in order to improve its performance: 1) Combining Fuzzy-Rough Dependency Degree with Correlation-based FS merit to deal with a dilemma situation in feature subset selection and 2) Hybridizing the newly proposed method with the threshold based FRQR. The effectiveness of the proposed approaches are proven over sixteen UCI datasets; smaller subsets of features and higher classification accuracies are achieved.

In this letter, we present a new method of alleviating the deterioration in the quality of real-time video service during vertical handover (VHO). The proposed method stochastically delays the starting time of the service disruption of VHO in order to reduce the number of lost frames caused by the inter-frame dependency of multi-layered video traffic. The results show that the proposed method significantly decreases the average frame loss time at the sacrifice of an increased handover execution time by one half of the group of picture (GOP) interval of the video traffic.

Many studies have reported that the single-event transient (SET) width increases with temperature. However, the mechanism for this temperature dependency is not clear, especially for an N-hit SET. In this study, TCAD simulations are carried out to study the temperature dependence of N-hit SETs in detail. Several possible factors are examined, and the results show that the temperature dependence in bulk devices is due to the decrease in the carrier mobility with temperature in both the struck NMOS and the pull-up PMOS. In contrast, the temperature dependence in SOI devices is due to the decrease in the diffusion constant and carrier lifetime with temperature, which enhances the parasitic bipolar effect.