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Comments in software are critical for maintenance and reuse. But apart from prescriptive advice, there is little practical support or quantitative understanding of what makes a comment useful. In this paper, we introduce the task of identifying comments which are uninformative about the code they are meant to document. To address this problem, we introduce the notion of comment entailment from code, high entailment indicating that a comment's natural language semantics can be inferred directlyarXiv:1806.04616v1 fatcat:yhyhprf2xra55bmqkkiqybvazq
more »... rom the code. Although not all entailed comments are low quality, comments that are too easily inferred, for example, comments that restate the code, are widely discouraged by authorities on software style. Based on this, we develop a tool called CRAIC which scores method-level comments for redundancy. Highly redundant comments can then be expanded or alternately removed by the developer. CRAIC uses deep language models to exploit large software corpora without requiring expensive manual annotations of entailment. We show that CRAIC can perform the comment entailment task with good agreement with human judgements. Our findings also have implications for documentation tools. For example, we find that common tags in Javadoc are at least two times more predictable from code than non-Javadoc sentences, suggesting that Javadoc tags are less informative than more free-form comments
Kumar, R.R. and Alok K  2020 Prospects for sustainability. ...doi:10.3390/su132212918 fatcat:4iqrhjwy6vdvrofeezzv2dkqsy
Malware evolves perpetually and relies on increasingly sophisticated attacks to supersede defense strategies. Datadriven approaches to malware detection run the risk of becoming rapidly antiquated. Keeping pace with malware requires models that are periodically enriched with fresh knowledge, commonly known as retraining. In this work, we propose the use of Venn-Abers predictors for assessing the quality of binary classification tasks as a first step towards identifying antiquated models. One ofdoi:10.1145/2996758.2996769 dblp:conf/ccs/DeoDSVC16 fatcat:cgx2kmrnfvdu7afi32e6guag2q
more »... the key benefits behind the use of Venn-Abers predictors is that they are automatically well calibrated and offer probabilistic guidance on the identification of nonstationary populations of malware. Our framework is agnostic to the underlying classification algorithm and can then be used for building better retraining strategies in the presence of concept drift. Results obtained over a timeline-based evaluation with about 90K samples show that our framework can identify when models tend to become obsolete.
Kumar Dash) operations is so vast that compilers might struggle to identify semantic errors. ... Dash et al.  used dual-channel constraints to mine conceptual types from identifiers and assignment flows between them. ...arXiv:2111.01577v1 fatcat:lwyygohljfel7bbvoscxb6n2jm
Amorphous Data Parallelism has proven to be a suitable vehicle for implementing concurrent graph algorithms effectively on multi-core architectures. In view of the growing complexity of graph algorithms for information analysis, there is a need to facilitate modular design techniques in the context of Amorphous Data Parallelism. In this paper, we investigate what it takes to formulate algorithms possessing Amorphous Data Parallelism in a modular fashion enabling a large degree of code re-use.doi:10.1109/hpcsim.2013.6641446 dblp:conf/ieeehpcs/DashSC13 fatcat:flipyyjd3fe7razg4pi4mk5e2i
more »... ing the betweenness centrality algorithm, a widely popular algorithm in the analysis of social networks, we demonstrate that a single optimisation technique can suffice to enable a modular programming style without loosing the efficiency of a tailor-made monolithic implementation.
With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely-on their own-be suitable for detecting obfuscated Android malware. To address this challenge, wedoi:10.1145/3029806.3029825 dblp:conf/codaspy/Suarez-TangilDA17 fatcat:tswhwmmpybhlzb4jnqz5xffvuq
more »... ropose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware. DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly. For malware detection, we achieve up to 99.82% accuracy with zero false positives; for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.
Intraoperative hypothermia is common issue with serious consequences occurred during anaesthesia. However, less attention has been directed to preventing redistribution hypothermia there is need of effective techniques to develop. Objective: In this study, we compared three different anaesthetic induction techniques to standard IV propofol inductions (control) in their effect on reducing redistribution hypothermia. Methods: Elective, afebrile patients, age 18 to 57 years, were randomly assigneddoi:10.33545/26643766.2018.v1.i2a.241 fatcat:yd5k3rnttbgihc2ki7z7c5jtuu
more »... to one of four groups (n = 60 each). Group "PROP" was induced with 2.2 mg/kg propofol, Group "INH/100" with 8% sevoflurane in 100% oxygen, Group "INH/50" with 8% sevoflurane in 50% oxygen and 50% nitrous oxide, and Group "Phnl/PROP" with 2.2 mg/kg propofol immediately preceded by 160 mcg phenylephrine. Patients were maintained with sevoflurane in 50% nitrous oxide and 50% oxygen in addition to opioid narcotic. Forced air warming was used. Core temperatures were recorded every 15 min after induction for 1 h. Results: Compared to control group PROP, the mean temperatures in groups INH/100, INH/50, and Phnl/PROP were higher 15, 30, 45 and 60 min after induction, averaging between 0.29 °C and 1.0 °C higher (p< 0.001 for all comparisons). There were statistically significant differences in the mean temperatures between groups INH/ 100 and INH/50, INH/100 and Phyl/PROP, and INH/50 and Phyl/PROP at any time point (all p< 0.05). Few patients in three groups had a core temperature >37.5 °C at T60 time point, except PROP group. Conclusions: The inhalation inductions with sevoflurane or with prophylactic phenylephrine bolus prior to propofol induction reduced the magnitude of redistribution hypothermia by an average of 0.29 to 1.0 °C in patients aged 18 to 57 years.
Smartphone platforms are becoming increasingly complex, which gives way to software vulnerabilities difficult to identify and that might allow malware developers to gain unauthorized privileges through technical exploitation. However, we maintain that these type of attacks indirectly renders a number of unexpected behaviors in the system that can be profiled. In this work we present CoME, an anomalybased methodology aiming at detecting software exploitation in Android systems. CoME models thedoi:10.1049/iet-ifs.2017.0460 fatcat:zen74jh2k5g7bkycjgtsm4ybv4
more »... rmal behavior of a given software component or service and it is capable of identifying any unanticipated behavior. To this end, we first monitor the normal operation of a given exploitable component through lightweight virtual introspection. Then, we use a multivariate analysis approach to estimate the normality model and detect anomalies. We evaluate our system against one of the most critical vulnerable and widely exploited services in Android, i.e., the mediaserver. Results show that our approach can not only provide a meaningful explanatory of discriminant features for illegitimate activities, but can also be used to accurately detect malicious software exploitations at runtime.
Lecture Notes in Computer Science
This paper presents a novel technique for power quality disturbance classification. Wavelet Transform (WT) has been used to extract some useful features of the power system disturbance signal and Gray-coded Genetic Algorithm (GGA) have been used for feature dimension reduction in order to achieve high classification accuracy. Next, a Probabilistic Neural Network (PNN) has been trained using the optimal feature set selected by GGA for automatic Power Quality (PQ) disturbance classification.doi:10.1007/978-3-642-11164-8_91 fatcat:3hdfye5usneq5bwfjl7wfwtwya
more »... dering ten types of PQ disturbances, simulations have been carried out which show that the combination of feature extraction by WT followed by feature reduction using GGA increases the testing accuracy of PNN while classifying PQ signals.
The surprising predictability of source code has triggered a boom in tools using language models for code. Code is much more predictable than natural language, but the reasons are not well understood. We propose a dual channel view of code; code combines a formal channel for specifying execution and a natural language channel in the form of identifiers and comments that assists human comprehension. Computers ignore the natural language channel, but developers read both and, when writing codedoi:10.1145/3377816.3381720 dblp:conf/icse/CasalnuovoBDDM20 fatcat:qo7d64qvafcdhj65djglhzft6y
more »... longterm use and maintenance, consider each channel's audience: computer and human. As developers hold both channels in mind when coding, we posit that the two channels interact and constrain each other; we call these dual channel constraints. Their impact has been neglected. We describe how they can lead to humans writing code in a way more predictable than natural language, highlight pioneering research that has implicitly or explicitly used parts of this theory, and drive new research, such as systematically searching for cross-channel inconsistencies. Dual channel constraints provide an exciting opportunity as truly multi-disciplinary research; for computer scientists they promise improvements to program analysis via a more holistic approach to code, and to psycholinguists they promise a novel environment for studying linguistic processes.
The Android ecosystem has witnessed a surge in malware, which not only puts mobile devices at risk but also increases the burden on malware analysts assessing and categorizing threats. In this paper, we show how to use machine learning to automatically classify Android malware samples into families with high accuracy, while observing only their runtime behavior. We focus exclusively on dynamic analysis of runtime behavior to provide a clean point of comparison that is dual to static approaches.doi:10.1109/spw.2016.25 dblp:conf/sp/DashSKTAKC16 fatcat:elql3s4vhbcnbhthjodtsyxizm
more »... Specific challenges in the use of dynamic analysis on Android are the limited information gained from tracking low-level events and the imperfect coverage when testing apps, e.g., due to inactive command and control servers. We observe that on Android, pure system calls do not carry enough semantic content for classification and instead rely on lightweight virtual machine introspection to also reconstruct Android-level interprocess communication. To address the sparsity of data resulting from low coverage, we introduce a novel classification method that fuses Support Vector Machines with Conformal Prediction to generate high-accuracy prediction sets where the information is insufficient to pinpoint a single family.
Non-steroidal anti-inflammatory drugs (NSAID) help relieve the discomfort of fever and reduce inflammation and associated pain. Post-surgery pain is most of the time unavoidable and NSAIDs are best strategies to combat the same as compared to opioids. Objective: In the present study we aimed to assess the analgesic efficacy and safety of paracetamol in comparison with diclofenac for postoperative pain relief when administered orally. Methods: Randomly selected 90 patients who underwentdoi:10.33545/26643766.2021.v4.i2a.234 fatcat:7juw4e2jlfamfis4pkgcferbve
more »... surgery under general anesthesia. Patients were divided into three groups to receive paracetamol (500 mg/kg), diclofenac (100 mg/kg) and placebo. Participants were assessed for the level of pain prior to treatment and upto 8 hrs posttreatment using visual analogue scale to score from 0-5. Statistical analysis of continuous data was done by unpaired t-test and one-way ANOVA. Results: Both paracetamol and diclofenac were effective for postoperative pain relief. We did not notice significant differences between paracetamol and diclofenac group at any time points, however, both treatments were effective in reducing pain score significantly from 1 hr to 6 hrs post treatment. Conclusion: Both paracetamol and diclofenac drugs are safe to provide analgesia through oral route in postoperative period and can be used irrespective of type and intensity of surgery without any major significant side effects.
ABSTRACTMalaria is a major public health problem in tropical and subtropical countries, including India. This study elucidates the cause of chloroquine treatment failure (forPlasmodium falciparuminfection) before the introduction of artemisinin combination therapy. One hundred twenty-six patients were randomized to chloroquine treatment, and the therapeutic efficacy was monitored from days 1 to 28. Anin vitrosusceptibility test was performed with all isolates. Parasitic DNA was isolated,doi:10.1128/aac.02762-14 pmid:25070111 pmcid:PMC4187921 fatcat:c23orskd4jc2lou7d3pmgxjety
more »... d by PCR and restriction digestion of different codons of thepfcrtgene (codons 72 to 76) and thepfmdr1gene (N86Y, Y184F, S1034C, N1042D, and D1246Y). Finally, sequencing was done to confirm the mutations. Forty-three (34.13%) early treatment failure cases and 16 (12.69%) late treatment failure cases were observed after chloroquine treatment.In vitrochloroquine resistance was found in 103 isolates (81.75%). Twenty-six (60.47%) early treatment failure cases and 6 (37.5%) late treatment failure cases were associated with the CVMNK-YYSNYallele (the underlined amino acids are those that were mutated). Moreover, the CVIEK-YYSNYallele was found in 8 early treatment failure (18.60%) and 2 late treatment failure (12.5%) cases. The presence of the wild-typepfcrt(CVMNK) andpfmdr1(YYSNY) double mutant allele in chloroquine-nonresponsive cases was quite uncommon.In vivochloroquine treatment failure andin vitrochloroquine resistance were strongly correlated with the CVMNK-YYSNYand CVIEK-YYSNYhaplotypes (P< 0.01).
Source code is bimodal: it combines a formal, algorithmic channel and a natural language channel of identiiers and comments. In this work, we model the bimodality of code with name lows, an assignment low graph augmented to track identiier names. Conceptual types are logically distinct types that do not always coincide with program types. Passwords and URLs are example conceptual types that can share the program type string. Our tool, RefiNym, is an unsupervised method that mines a lattice ofdoi:10.1145/3236024.3236042 dblp:conf/sigsoft/DashAB18 fatcat:3aypqmdxubfm5eq7iizzhkz3my
more »... nceptual types from name lows and reiies them into distinct nominal types. For string, RefiNym inds and splits conceptual types originally merged into a single type, reducing the number of same-type variables per scope from 8.7 to 2.2 while eliminating 21.9% of scopes that have more than one same-type variable in scope. This makes the code more self-documenting and frees the type system to prevent a developer from inadvertently assigning data across conceptual types.
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