In this paper, by using a visualization tool that we developed, the users may view this tool as helpful because of selection of many sentiment analysis reviews in the list, not reading all the reviews of the existing list.

scores also obtained by 3 points from negative words such as "evil".


System must extract one or more ASIN(Amazon Standard Identification Number) of the movie title by user's choice[10].

system obtains ASIN from Amazon ASIN Database that is established in advance.


Each miner receiving a newly created block validates the block's integrity.

it makes sure that (i) the block's SHA-2562 is indeed lower than the difficulty threshold, (ii) all transactions in it are legitimate (i.e., if all input coins are in the UTxO set and are signed by their claimed owners), and that (iii) the miner that created it has allocated the correct amount to his own wallet (i.e., the number of bitcoins offered per created block, plus the fees of all included transactions).


A block is indexed in the DHT by its own hash value.

we maintain an array of all blocks’ hashes, which we call the index.


Request graphs tend to be smaller as the number of different requests is considerably smaller than the number of different objects.

every cache is aware of other cache contents using a replicated directory.


The underlying idea of business domain orientation, fostered by these patterns, is also an integral part of the Star pattern.

in contrast to the Star pattern, where representational connectors are mandatory, no such building block exists in DDD.


Performance The service should be capable of delivering high-quality audio within acceptable latency bounds.

the latency between audio device sources should be less than that of the human hearing detectability threshold of 50ms for speech [6].


There is no evidence there is a difference between the underlying mean latencies for the different number of channels in surround sound mode (p-value=0.12).

for the number of channels tested in this experiment, it cannot be claimed that the number of channels has an effect on listener to listener latency.


In contrast, a careful discussion is needed for isEmpty, as there is no computable counterpart of isEmpty in general functional programs, and it must be converted to an productivity-test oracle (Section 4).

unlike that in Section 4, the discussions in Sections 5, 6 and 7 can be easily extended to isEmpty because isEmpty becomes computable for FUnCAL programs thanks to the type system in Section 6.


Those components, which are primarily developed as a part of applications, are integrated within the modeling tool.

several end-user applications, generated in the run-time, are simultaneously directed towards single target RTS and instance of a program being debugged.


The TCP/IP channels, or remote consoles are used to access remote RTSs.

in the model-level debugging, purpose of the sub-model construction is to: 1) reduce the set of application states to the level that it can be visually traced; 2) vary values of object included in a model in an efficient and simple way; and 3) generate representative documentation.


The visual graph-node-arc convention surreptitiously introduces the user to the concept of type by requiring him to match the type of the output of an upstream node with the type expected as input to a downstream node.

visual programming can become extremely verbose and does not scale to more complex programing tasks.


Vaniea et al. [40] already found that users rely on memories of previous update experiences when making future update decisions and that the duration of upgrades seems to have some impact.

we wanted to better investigate the link between actual experience, memory, and future decisions.


Participants expressed an interest in hoarding code through commenting out code snippets and refusing to delete old source code files in case their exploration did not pay off.

this lead to confusion with keeping track of multiple similar copies as well as commented versus not-commented code.


Unlike conventional version control, these informal practices allow for fast versioning of any size code snippet, and quick comparisons by interchanging which versions are run.

data scientists must maintain a strong mental map of their code in order to distinguish versions, leading to errors and confusion.


While viewing an earlier commit, Ellen cannot edit the code, but she can re-run it, copy it, or create a new branch from that point in time.


In this paper, we present a new empirical study that both addresses our goal of principles as well as overcomes the limitations of prior Patchworks evaluations.

we performed an observational user study that compared the ubiquitous tabbed editor design with the experimental Patchworks design to inductively build a set of design principles.


Non-temporary assignment is a separate instruction, because it involves complex logic in the general case (destroying the previous value safely, handling reference assignments, handling overloaded assignment operators, etc.)

no bytecode representation is used, instead all operations are performed on the AST level.


For example, the Starkiller project [20] translates Python code to C++, using an augmented Cartesian product algorithm [21] for type inference.

this approach is often not able to support all language semantics.


[23] compiles a functional language (Scheme) into a dataflow representation to produce custom hardware.

they only implement strict functions and do not exploit the pipelined parallelism we can.


Professional, novice, or amateur designers, are now equipped with the means to fabricate and test ideas rapidly at a relatively low cost, and at a much earlier stage of the design process.

just like any new tools, integrating digital fabrication into the design process is often a trial-and-error process such as specifying the right joints and parameters for different materials during laser cutting.


In every field, the correct tools are needed to complete a job or tasks.

in mobile digital forensics, software is one of the most valuable tools.


When the GPU driver receives a request for GPU memory that cannot be satisfied, GPrioSwap first selects a victim which must give up some of its memory.

either the user space libraries or the applications themselves must be modified to support these new APIs.


To that end, we need to count accesses to individual pages with sufficient accuracy.

as we explain in Section 2, current GPUs do not support counting accesses to individual pages out of the box.


This hybrid approach was informed by earlier findings on the design of block-based tools, including features that learners found to be useful as well as perceived drawbacks of block-based programming tools [36].

this hybrid design retains features including a browsable blocks library, drag-and-drop composition, and pre-fabricated commands.


Secondly, maybe students contribute only for a short period and then drop out of the project, while employed contributors stick with the project after their first contributions.

later samples of OSS contributors would cover the employed contributors who are still with the project, but not the students who had left after their first contributions already.


Core contribution barriers like Solution are obviously necessary for the modification and therefore less likely to be underestimated; thus, we operationalize the unexpected effort via the secondary contribution barriers Download, Setup, and Build.

there are no statistically significant differences for these contribution barriers.


An additional wrinkle is that it is much more difficult to find spam projects on SourceForge using the downloads metric, as we did with CodePlex, due to inconsistencies in the way data was donated to SRDA over the years [13], and the lamentable fact that there seems to be no accurate mechanism for querying downloads after 2009.

we differentiate between all projects and and likely nonspam projects using the amount of text in the description field, since legitimate projects will include a description, even if it is short.


By looking at the results reported by the accuracy measure and F1measure, we can see a discrepancy between them, possibly due to the sample size and, more importantly, our use of 10-way cross validation, which usually overestimates errors in models [20].

the real performance of the models in F1-measure exceeds the values reported in Table 6.


This result indicates that algorithm factor contributes to increased pricing.

it might reveal more complexity or some constraints within the task.