The feelings of rejected papers __ the inner monologue

The result has always been expected, but when the results really appear in front of their eyes, the heart is still a bit of a faint bitterness. The thought of a miraculous appearance was completely extinguished. When you calm down, read the manuscript carefully, a bit distressed reviewers. Because the level of your writing is really bad. It's not easy for reviewers to read my paper, not to mention so much guidance, and I thank them. At the same time, through their suggestions, I found that my work can be more in-depth research, sadness and joy.

Remind yourself later, write the paper, no matter whether the innovation point is excellent, to ensure that the content can make readers understand, read more comfortable. Logically reasonable, content enrichment, technical details, experimental results accurate. One aspect is responsibility for your own work, and the other is respect for readers and reviewers. The reader (reviewer) cannot be upset because of one of his papers. Haha, continue to refuel.

In a hurry to graduate at the same time, also hope that they can calm down, and carefully carve their own work, so that every job can afford to pay their own time and effort. I would like to encourage myself and alert myself. To share with like-minded people, may we all be the capital of the people.

The following appendix review comments (see these, what do you think?) ）：

======= Review 1 =======

> * * * recommendation:please provide youroverall assessment of the paper.
Likely Accept (this paper should is accepted Buti'll not champion it) (4)

> * * * * expertise:please indicate your levelof expertise with the topic of the paper.
Basic Knowledge (2)

> * * * * Summary:what are the major issuesaddressed in the paper? Do you consider them important? Comment on the novelty,creativity, impact, presentation, and technical depth.

This paper studies the problem of activityrecognition using CSI information to a single WiFi AP. This is a wellinvestigated, yet still open, problem. A holistic approach ispresented including coarse-grained activity recognition, detection of the start andend of activity, and Distinguishing between independent and continuousactivities. These are all important aspects to the general recognition problem. The implemented system and experimentation validate the accuracy ofthe proposed approach and can significantly impact Re works in this area.

> * * * strength:please discuss the mainstrengths of this paper.

1. The paper is-written (with few typosonly outlined below) and structured. The problem is nicely motivated while thedescription to related work is sufficient.
2. Solution methods for different problems arepresented such as the DWT, window-based moving and SRC variance.
3. Various practical challenges faced whendesigning a wifi-based recognition system are taken into consideration. Theseinclude the subcarrier sensitivity, speed, location, height of volunteer, etc.
4. The implementation and Experimentationresults are extensive. They clearly show the impact of different systemparameters on the recognition accuracy.

> * * * weakness:please discuss the mainweaknesses of this paper.

1. A weakness of the paper is this itstheoretical contribution is rather limited. To a large extent, the paper isbased on previous methods for dealing and theoretical activity recognitionproblems, e.g., Classic classification algorithms.
2. The presented results are limited to one apand certain of types of rooms. It is unclear if the reported accuracy Ofrecognition would remain at more realistic conditions.
3. Additional evaluation results that comparewith state-of-the art methods could have been provided to strengthen the Pape R.
4. Additional explanation is needed about thedifferences between the values in table Ii.
5. Some language errors in the text are thefollowing:
-previous Works faces->previous (page 2)
-we proposed Car->we propose car (page 2)
-therefore, We->therefore, We (page 4)
-is more Robustness->is Robust (page 4)

> * * * * Detailed comments:please providedetailed Comments that'll help the TPC assess the paper and help PROVIDEFEEDBA CK to the authors. High quality review comments are extremely importantfor the discussion in the TPC meeting.

Please detailed comments above.

======= Review 2 =======

> * * * recommendation:please provide youroverall assessment of the paper.
Likely Accept (this paper should is accepted Buti'll not champion it) (4)

> * * * * expertise:please indicate your levelof expertise with the topic of the paper.
Familiar (3)

> * * * * Summary:what are the major issuesaddressed in the paper? Do you consider them important? Comment on the novelty,creativity, impact, presentation, and technical depth.

The paper presents car, a csi-basedcoarse-grained activity recognition using a single AP.
It used CSI to detect activities in indoorenvironments by exploiting advanced classification on DWT. It divides THECSI sequence into multiple segments and with the combination of multiplesegments to increase the Classificati On accuracy. It was provided effective.

> * * * strength:please discuss the mainstrengths of this paper.

+ It is challenging and also useful to enablecoarse-grained activity recognition using a single AP.
+ The proposed algorithm is effective andoutperforms the state of the art.
+ The work is written and executed.

> * * * weakness:please discuss the mainweaknesses of this paper.



> * * * * Detailed comments:please providedetailed Comments that'll help the TPC assess the paper and help PROVIDEFEEDBA CK to the authors. High quality review comments are extremely importantfor the discussion in the TPC meeting.

I enjoy reading this paper. This is the Writtenand executed.
The proposed solutions leverages the combinationof multiple segments to effectively improves the classification.

Here are several questions regarding itssensitive to data quality.
(1) would car handle samples obtained fromdifferent locations OK? CSI should vary at locations. So have you consideredit? Have your tested with training's samples from one location and test samplesfrom another? Without addressing it, car ' s application is quitelimited.

(2) Have you notice the impact of Differentuser? Compared with fine-grained activity, coarse-grained activity can lowerits sensitive to test users with different height an D figures. However, Haveyou considered them? IS are user-dependent classification or a generic one wheresamples from different users are mixed?

(3) I assume that the paper would consider theuser perform activity at one specific location. No more mobility would beconsidered. Please clarify it. I guess that car may work poor under mobility.

Without considering the above factors, car maybe still useful but it practicality is limited. It should be addressed in theeval or in the discussion.

======= Review 3 =======

> * * * recommendation:please provide youroverall assessment of the paper.
Definite Reject (I'll fight against Acceptingthis paper at the TPC Meeting) (1)

> * * * * expertise:please indicate your levelof expertise with the topic of the paper.
Familiar (3)

> * * * * Summary:what are the major issuesaddressed in the paper? Do you consider them important? Comment on the novelty,creativity, impact, presentation, and technical depth.

In this paper, the authors develop a system calledcar to recognize coarse-grained. In fact, many CSI based activityrecognition schemes have been proposed in recent years. The main contributionin this paper are to segment the continuous activities, which is a practicalissue. However, the lack of several important details makes it confused tounderstand the ideas. Presentation and technical depth are required to improvein this paper.

> * * * strength:please discuss the mainstrengths of this paper.

1. The authors implement their system underdifferent scenarios and compare the results with different.

> * * * weakness:please discuss the mainweaknesses of this paper.

1. In this paper, one main contribution is tosegment activities. To detect the direction of a activity, the authors claimthat they explore "difference between antennae on the signal p Attern. However, they do does not explain you to parse direction information. Meanwhile,related experiment is missing.

2. There are several serious errors inexperiment. For example, in Page 7, the authors claim the accuracy OFNB algorithm are 64% (50%), which is isn't in agreement with the ResU Lts shown infigure (b); In table III, the sum of probability of error and correct was not equal to1 for label 3,8 and 16.
3. The presentation of experiment results isvague. For example, in table III, they does not give the correspondence betweendifferent activities and labels. Meanwhile, the authors claim that THEACTIVITY13 are recognized with the accuracy error 12.79% as the Activity7 withoutanaly Zing reasons.
4. The tables and figures in this paper are notprofessional. For example, figure seems a little informal; The bold font intable II has not been explained; The line between label 7 and 8 seems lessmeaningful.

> * * * * Detailed comments:please providedetailed Comments that'll help the TPC assess the paper and help PROVIDEFEEDBA CK to the authors. High quality review comments are extremely importantfor the discussion in the TPC meeting.

1. In page 5, the authors claim this they explorethe difference between antennae on the signal pattern to detect the Direc Tionof an activity. However, they don't explain to parse directioninformation. Meanwhile, related experiment is missing.
2. In page 6, figure seems a little informal. Meanwhile, the experiment results under different scenarios, with and withoutempty baseline environment, are.

3. In page 7, the authors claim the accuracy OFNB algorithm are 64% (50%), which is isn't in agreement with the results shown Infigure (b).

4. In Page 7, the bold font in table II has Notbeen explained.

5. In page 8, the sum of probability of errorand correct isn't equal to 1 for label 3,8 and in table Iii.

======= Review 4 =======

> * * * recommendation:please provide youroverall assessment of the paper.
Definite Reject (I'll fight against Acceptingthis paper at the TPC Meeting) (1)

> * * * * expertise:please indicate your levelof expertise with the topic of the paper.
Basic Knowledge (2)

> * * * * Summary:what are the major issuesaddressed in the paper? Do you consider them important? Comment on the novelty,creativity, impact, presentation, and technical depth.

The paper introduce a Channel state information (CSI)-based coarse-grained activity recognition system named car.  Authors havethe objective of finding the signal pattern corresponding to the specificactivity. The evaluation is conducted through experiments. Experiment
Results with one user show this car systemachieves a average accuracy
of 93.35%

> * * * strength:please discuss the mainstrengths of this paper.

Use the joint method of discrete Wavelet (DWT) and window-based moving variance to determine the precise start and theen D of activities data corresponding to the activity.

Design Alpha to distinguish an independentactivity from continuous and classifier based on the Sparsereprese Ntation classifier (yet already used in recent works).

Implementation of the system in differentsettings.

> * * * weakness:please discuss the mainweaknesses of this paper.

The ideas are presented chaotically and are hardto.

Using variations of the sub-carriers todescribe the human activity is not new.

The paper is overall poorly written.

> * * * * Detailed comments:please providedetailed Comments that'll help the TPC assess the paper and help PROVIDEFEEDBA CK to the authors. High quality review comments are extremely importantfor the discussion in the TPC meeting.

In Figure 2, it's unclear what carrier of THECSI is plotted; Or are the results output of some filter over multiplecarriers?

Results in Fig 3 (b) are expected by the spectralcoherence of the channel.

There are a few typos, such as "an activityis consists of ...", "We Collect data is real-time", "according the J value", "Th E following works, we Willexplore ",...

Section II.3 about the design framework isconfusing and a repetition of things this have been already said in Previoussect ions.

======= Review 5 =======

> * * * recommendation:please provide youroverall assessment of the paper.
Likely Reject (this paper should is rejected Buti'll not fight strongly against it) (2)

> * * * * expertise:please indicate your levelof expertise with the topic of the paper.
Expert (4)

> * * * * Summary:what are the major issuesaddressed in the paper? Do you consider them important? Comment on the novelty,creativity, impact, presentation, and technical depth.

This paper proposes a csi-based coarse-grainedactivity recognition system in indoor.
The proposed scheme leverages a series of Novelmethods to synchronize, segment and classify different activities and can B Eused to monitor continuous activity.
The authors claim that their work can achieve anaverage accuracy of the 93.35% to activity coarse-grained.

> * * * strength:please discuss the mainstrengths of this paper.

(1) The idea to monitor continuous activityrather than identical an activity to make the State-of-the-art advances In practical use is impressive and convincing.
(2) The proposed methods seem promising andprogressive.
For example, the authors propose a joint methodof the DWT and window-based moving to variance the "start and End" Ofan activity.
(3) The claimed evaluation result looks good andseems to being a step further to the existing works related to csi-based activity Recognition.

> * * * weakness:please discuss the mainweaknesses of this paper.

(1) The authors should focus is more on thecontributions of their work.
For example, to detect the boundaries ofactivity, that joint method of the DWT and window-based moving variance As a fairly straightforward endeavor.
It would vastly help the reader to introduce thedesign decisions and choices made.
(2) The author claims to have designed a methodto detect activity, continuous but is not have introduced.
(3) In the segmenting part, the author mentionedthat Various directions of sub-activities produce a slight change in the Signa Lpattern, but didn ' t introduce how the "is" like,
Which makes this solution less convincing.
(4) From the above 1~3, the contribution isover-claimed, this work seems to be a combination of existing methods.
(5) The recognition process seems to be doneoff-line, which makes the system less applicable.

> * * * * Detailed comments:please providedetailed Comments that'll help the TPC assess the paper and help PROVIDEFEEDBA CK to the authors. High quality review comments are extremely importantfor the discussion in the TPC meeting.

The quality of writing is poor. There are toomany vocabulary errors and grammatical errors.
For example, "The consists Ofsome ..." in Page 2, "We collect the data is real-time andaccidental ..." in Page 3.
The errors are too numerous to count, and Evenreduce readers ' comprehension.
The paper put too more weight on backgroundinformation rather than the technical details, making the proposed solutionsles s convincing
The observations introduced in part II. A Seemsto is presented just to increase the contributions of this paper.
I can not find any relationship between Theseobservations and the solutions proposed.
The segmenting activity algorithm are vague andshould be introduced more detailedly.