Reviewerly/exHarmony huggingface dataset:
Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies

Automatic Ingestion Monitor v2 (AIM-2) is an egocentric camera and sensor that aids monitoring of individual diet and eating behavior by capturing still images throughout the day and using sensor data to detect eating.The images may be used to recognize foods being eaten, eating environment, and other behaviors and daily activities.At the same time, captured images may carry privacy concerning content such as (1) people in social eating and/or bystanders (i.e., bystander privacy); (2) sensitive documents that may appear on a computer screen in the view of AIM-2 (i.e., context privacy).In this paper, we propose a novel approach based on automatic, image redaction for privacy protection by selective content removal by semantic segmentation using a deep learning neural network.The proposed method reported a bystander privacy removal with precision of 0.87 and recall of 0.94 and reported context privacy removal by precision and recall of 0.97 and 0.98.The results of the study showed that selective content removal using deep learning neural network is a much more desirable approach to address privacy concerns for an egocentric wearable camera for nutritional studies.
UTILIZING WHATSAPP APPLICATION FOR TEACHING INTEGRATED ENGLISH (A CASE STUDY AT UNIVERSITY OF TECHNOLOGY YOGYAKARTA)

This study aimed to illustrate some learning activities in Integrated English class byutilizing WhatsApp application and how the students respond to its implementation. The study was conducted in Accounting Department at University of Technology Yogyakarta. The subject of this study was 66 students in Accounting Class A of 2018/2019 Academic Year. The instruments to gather the data were observation, questionnaires and unstructrured interview. The result of this study showed that WhatsApp Application could be utilized as one of the beneficial devices to assist students in learning integrated English and there were positive responses from students during the implementation of WhatsApp in their learning process.
Information Security Risk Management with Octave Method and ISO/EIC 27001: 2013 (Case Study: Airlangga University)

Airlangga University has implemented ISO 27001: 2013 in asset-based information security governance, covering information assets, software assets, hardware assets, and human resources assets. However, many vulnerabilities in university computing systems can not be mitigated properly, as evidenced by the continued hacking of university computing systems. It shows that the results of hacking tests on university computing systems are not identified in more detail and are not included in university risk management. The purpose of this research is to build a university information security risk management framework using OCTAVE method based on ISO / EIC 27001: 2013. This research uses the OCTAVE framework to build a risk management framework model. The measurement method will be done by qualitative method to measure the severity and the likelihood of each asset and quantitative method to measure the potential loss on the cost of each asset. The results of this research are expected to provide an information security risk management framework, so that the vulnerability and financial lost analysis of each asset can be a risk, and risk mitigation plans on each asset may consider vulnerability and return of investment
Hybrid quantum network design against unauthorized secret-key generation, and its memory cost

A significant number of servers that constitute the Internet are to provide private data via private communication channels to mutually anonymous registered users. Such are the servers of banks, hospitals that provide cloud storage and many others. Replacing communication channels by maximally entangled states is a promising idea for the quantum-secured Internet (QI). While it is an important idea for large distances secure communication, for the case of the mentioned class of servers pure entanglement based solution is not only unnecessary but also opens a threat. A crack stimulating a node to generate secure connections via entanglement swapping between two hackers can cause uncontrolled consumption of resources. Turning into positive a recently proven no-go result by S. Bäuml et al. [Nat. Commun. 6, 6908 (2015)], we propose a natural countermeasure against this threat. The solution bases on connections between hub-nodes and end-users realized with states that contain secure key but do not allow for swapping of this key. We then focus on the study of the quantum memory cost of such a scheme and prove a fundamental lower bound on its memory overhead. In particular, we show that to avoid the possibility of entanglement swapping, it is necessary to store at least twice as much memory than it is the case in standard quantum-repeater-based network design. For schemes employing either states with positive partial transposition that approximates certain privates states or private states hardly distinguishable from their attacked versions, we derive much tighter lower bounds on required memory. Our considerations yield upper bounds on a two-way repeater rate for states with positive partial transposition (PPT), which approximates strictly irreducible private states. As a byproduct, we provide a lower bound on the trace distance between PPT and private states, shown previously only for private bits.6 MoreReceived 26 March 2020Accepted 30 July 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043022Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum cryptographyQuantum entanglementQuantum memoriesQuantum networksQuantum repeatersQuantum Information
The Extraordinary Failure of Complement Coercion Crowdsourcing

Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years.In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon.These are constructions with an implied action -e.g., "I started a new book I bought last week", where the implied action is reading.We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference.However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work.Why does the same process fail to yield high agreement scores?We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure.We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.
Counting single-qubit Clifford equivalent graph states is #P-complete

Graph states, which include for example Bell states, GHZ states and cluster states, form a well-known class of quantum states with applications ranging from quantum networks to error-correction. Deciding whether two graph states are equivalent up to single-qubit Clifford operations is known to be decidable in polynomial time and have been studied both in the context of producing certain required states in a quantum network but also in relation to stabilizer codes. The reason for the latter this is that single-qubit Clifford equivalent graph states exactly corresponds to equivalent stabilizer codes. We here consider the computational complexity of, given a graph state |G>, counting the number of graph states, single-qubit Clifford equivalent to |G>. We show that this problem is #P-Complete. To prove our main result we make use of the notion of isotropic systems in graph theory. We review the definition of isotropic systems and point out their strong relation to graph states. We believe that these isotropic systems can be useful beyond the results presented in this paper.
Event co-reference resolution via a multi-loss neural network without using argument information

Event coreference resolution(ECR) is an important task in Natural Language Processing (NLP) and nearly all the existing approaches to this task rely on event argument information. However, these methods tend to suffer from error propagation from the stage of event argument extraction. Besides, not every event mention contains all arguments of an event, and argument information may confuse the model that events have arguments to detect event coreference in real text. Furthermore, the context information of an event is useful to infer the coreference between events. Thus, in order to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not need any argument information to do the within-document event coreference resolution task and achieve a significant performance than the state-of-the-art methods.
LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets

In this work, we describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets.Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets.We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic.Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set.
On those multiplicative subgroups of $${\mathbb F}_{2^n}^*$$ which are Sidon sets and/or sum-free sets

We study those multiplicative subgroups of $${\mathbb F}_{2^n}^*$$ which are Sidon sets and/or sum-free sets in the group $$({\mathbb F}_{2^n},+)$$ . These Sidon and sum-free sets play an important role relative to the exponents of APN power functions, as shown by a paper co-authored by the first author.
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields

Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation.Existing approaches usually formulate this as a sequence labeling task of predicting whether there is a dropped pronoun before each token and its type.Each utterance is considered to be a sequence and labeled independently.Although these approaches have shown promise, labeling each utterance independently ignores the dependencies between pronouns in neighboring utterances.Modeling these dependencies is critical to improving the performance of dropped pronoun recovery.In this paper, we present a novel framework that combines the strength of Transformer network with General Conditional Random Fields (GCRF) to model the dependencies between pronouns in neighboring utterances.Results on three Chinese conversation datasets show that the Transformer-GCRF model outperforms the state-of-the-art dropped pronoun recovery models.Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

In the past decade, a transform called all phase discrete cosine biorthogonal transform (APDCBT) appeared in the field of digital image processing. It is mainly used to solve the negativity found in discrete cosine transform (DCT), especially in a low bit rate. In this paper, the APDCBT is employed for watermark insertion based on selected regions of an image. The insertion is depending on the homogeneity of each part of the image. To determine the extent of homogeneity, there are two criteria: Mean and standard deviation are applied for the intensity of the image. Medium frequency bands of APDCBT in image pixel blocks are used to hold the embedded watermark. Then, the transform is inversed to obtain the resultant watermarked image. The robustness of APDCBT against watermark removal attacks is tested and the experimental results showed the superiority of the APDCBT over traditional DCT in the watermark embedding system. More specifically, when the LSB reset attack applied. For instance, when the contrast adjustment attack applied, the average of normalized cross-correlation (NCC) values of extracted watermark images with the original watermark, it was 0.992 for the proposed method. This is a promised result, if it is compared with the NCC of the DCT method, which was 0.423. The proposed method can be used for copyright protection purpose.
Odds ratios and Mantel-Haenszel quotients

In 2018 Bornmann and Haunschild (2018a) introduced a new indicator called the Mantel-Haenszel quotient (MHq) to measure alternative metrics (or altmetrics) of scientometric data. In this article we review the Mantel-Haenszel statistics, point out two errors in the literature, and introduce a new indicator. First, we correct the interpretation of MHq and mention that it is still a meaningful indicator. Second, we correct the variance formula for MHq, which leads to narrower confidence intervals. A simulation study shows the superior performance of our variance estimator and confidence intervals. Since MHq does not match its original description in the literature, we propose a new indicator, the Mantel-Haenszel row risk ratio (MHRR), to meet that need. Interpretation and statistical inference for MHRR are discussed. For both MHRR and MHq, a value greater (less) than one means performance is better (worse) than in the reference set called the world.
Towards Refinable Choreographies

We investigate refinement in the context of choreographies. We introduce refinable global choreographies allowing for the underspecification of protocols, whose interactions can be refined into actual protocols. Arbitrary refinements may spoil well-formedness, that is the sufficient conditions that guarantee a protocol to be implementable. We introduce a typing discipline that enforces well-formedness of typed choreographies. Then we unveil the relation among refinable choregraphies and their admissible refinements in terms of an axiom scheme.
IR&amp;TM-NJUST@CLSciSumm 20

This paper mainly introduces our methods for Task 1A and Task 1B of CL-SciSumm 2020.Task 1A is to identify reference text in reference paper.Traditional machine learning models and MLP model are used.We evaluate the performances of these models and submit the final results from the optimal model.Compared with previous work, we optimize the ratio of positive to negative examples after data sampling.In order to construct features for classification, we calculate similarities between reference text and candidate sentences based on sentence vectors.Accordingly, nine similarities are used, of which eight are chosen from what we used in CL-SciSumm 2019 and a new sentence similarity based on fastText is added.Task 1B is to classify the facets of reference text.Unlike the methods used in CL-SciSumm 2019, we construct inputs of models based on word vectors and add deep learning models for classification this year.
Correlation-Aware Next Basket Recommendation Using Graph Attention Networks

With the increasing number of commodities in our daily life, the recommender system plays a more and more important role in selecting items of users’ interests. For the next basket recommendation task, in this work, we propose the first end-to-end correlation-aware model to predict the next basket considering intra-basket correlations using graph attention networks. Specifically, items and correlations between items are viewed as nodes and edges in a graph, respectively. By estimating and aggregating the intra-basket correlations using the attention layer of the self-attention model, the recommendation can be conducted at the basket level, instead of at the item level. We conduct comprehensive experiments on a real-world retailing dataset to show the improvement from state-of-the-art baselines using our proposed method.
Characteristic functionals of Dirichlet measures

We compute characteristic functionals of Dirichlet-Ferguson measures over a locally compact Polish space and prove continuous dependence of the random measure on the parameter measure.In finite dimension, we identify the dynamical symmetry algebra of the characteristic functional of the Dirichlet distribution with a simple Lie algebra of type A. We study the lattice determined by characteristic functionals of categorical Dirichlet posteriors, showing that it has a natural structure of weight Lie algebra module and providing a probabilistic interpretation.A partial generalization to the case of the Dirichlet-Ferguson measure is also obtained.
Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model

Mapping local news coverage from textual content is a challenging problem that requires extracting precise location mentions from news articles.While traditional named entity taggers are able to extract geo-political entities and certain non geo-political entities, they cannot recognize precise location mentions such as addresses, streets and intersections that are required to accurately map the news article.We fine-tune a BERT-based language model for achieving high level of granularity in location extraction.We incorporate the model into an end-to-end tool that further geocodes the extracted locations for the broader objective of mapping news coverage.
Active Phase Stabilization for the Interferometer With 128 Actively Selectable Paths

A variable-delay optical interferometer with 128 actively selectable delays and a technique of active phase stabilization are innovatively designed and applied for the first time in the experiment of round-robin differential phase shift quantum key distribution (RRDPS-QKD). According to the RRDPS protocol, larger number of delay channels in interferometer can ensure higher tolerance of bit errors, eliminating the fundamental threshold of bit error rate of 11% of traditional BB84 protocol. Considering that the implementation of RRDPS protocol relys on the realizaiton of the interferometer, therefore, an interferometer with 128 selectable delay paths is constructed and demands the ability of fast switching at the rate of 10 kHz, which requires dynamic stability of multiple paths. Thus, a specific designed phase stabilization technique with closed real time feedback loop is introduced to guarantee the high visibility of interferometer selections dynamically. The active phase stabilization technique employs a phase modulator (PM) driven by a DAC to adjust the relative phase between the two arms of the interferometer. By monitoring photon counting rates of two Up-Conversion Detectors (UCD) at two output ports of the interferometer, a Field Programmable Gate Array (FPGA) calculates and finds the optimal code value for the DAC, maintaining a high visibility of the interferometer every time a new light path is selected. The visibility of most of the 128 interferometer selections can simultaneously be maintained over 96% during the QKD, which lays the foundation for the RRDPS experiment.
Student’s response in online learning process: A case study of English Education students

The purpose of this study was to look at the level of student activity in online lectures. This research method uses descriptive qualitative. In this research, the data source is all online lecture recording one of the colleges in Madiun contained in the online media platform used by the author. The research results found several exciting things that the level of activity of students in the discussion is excellent compared with offline lectures. Several things can affect, such as the level of student confidence, comfort in attending lectures, and enough time to read the material provided can be longer so that they can understand the material provided. So it can be concluded that online lectures are convenient for some things to apply in education.
Copy-move forgery detection using convolutional neural network and K-mean clustering

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.
Meningkatkan Hasil Belajar Siswa Pada Mata Pelajaran Kemuhammadiyahan Melalui Model Pembelajaran Snowball Throwing Di Madarasah Aliyah Muhammadiyah 1 Medan

The purpose of this study was to determine student learning outcomes using the Snowball Throwing method on the subject of the Muhammadiyah Academy in Madarasah Aliyah Muhammadiyah 1 Medan. This research is expected to be useful for teachers, schools and future researchers. For teachers, the results of this study are expected to be useful for teachers to improve teaching skills, especially in terms of innovative learning models. The methodology in this research is field research that is obtained through classroom action research (CAR) or called classroom action research. This research method uses two cycles. In the first cycle consists of the planning phase (planning), the stage of action / implementation (action), the stage of observation (observation), and the stage of reflection. Then in the second cycle carried out after learning in the first cycle was analyzed and reflected in order to correct the deficiencies that existed in cycle I. The results showed that the Class X learning outcomes using the Snowball Throwing model were higher than those who did not use them.
Precedent Comparison in the Precedent Model Formalism: A Technical Note

We outline a formalization of precedent comparison in the precedent model formalism.