Reviewerly/exHarmony huggingface dataset:
Backdoor Attacks on the DNN Interpretation System

Interpretability is crucial to understand the inner workings of deep neural networks (DNNs). Many interpretation methods help to understand the decision-making of DNNs by generating saliency maps that highlight parts of the input image that contribute the most to the prediction made by the DNN. In this paper we design a backdoor attack that alters the saliency map produced by the network for an input image with a specific trigger pattern while not losing the prediction performance significantly. The saliency maps are incorporated in the penalty term of the objective function that is used to train a deep model and its influence on model training is conditioned upon the presence of a trigger. We design two types of attacks: a targeted attack that enforces a specific modification of the saliency map and a non-targeted attack when the importance scores of the top pixels from the original saliency map are significantly reduced. We perform empirical evaluations of the proposed backdoor attacks on gradient-based interpretation methods, Grad-CAM and SimpleGrad, and a gradient-free scheme, VisualBackProp, for a variety of deep learning architectures. We show that our attacks constitute a serious security threat to the reliability of the interpretation methods when deploying models developed by untrusted sources. We furthermore show that existing backdoor defense mechanisms are ineffective in detecting our attacks. Finally, we demonstrate that the proposed methodology can be used in an inverted setting, where the correct saliency map can be obtained only in the presence of a trigger (key), effectively making the interpretation system available only to selected users.
Aggregate Message Authentication Code Capable of Non-Adaptive Group-Testing

We introduce group-testing aggregate message authentication code (GTA MAC) and provide its formal study. We first specify its syntax and security requirements. Then, we present a scheme of generic construction which applies non-adaptive group-testing to aggregate MAC. We also confirm the security of the generic construction based on that of underlying aggregate MAC and a useful property of matrices representing non-adaptive group-testing. In addition, we instantiate the generic construction using the aggregate MAC scheme proposed by Katz and Lindell or a scheme using a cryptographic hash function for aggregating tags. Finally, we present some implementation results to show the effectiveness of our proposed GTA MAC.
Knowledge-Oriented Models Based on Developer-Artifact and Developer-Developer Interactions

INTRODUCTION: Software development is organized around developers working collaboratively promoting two types of interactions for knowledge sharing.Developer-Artifact interactions indicate developers define or access pieces of information within artifacts.Developer-Developer interactions indicate the exchange of information among developers using a collaboration platform to clarify an issue, promote an idea, or expose any thoughtful comment.PROBLEM: The number of such interactions grows over time and makes it difficult to capture and assess the evolution of the developers' knowledge about specific software project artifacts and tasks.Further, this knowledge decreases over time due to the natural limitations of human cognition that restrict our capabilities to cope with information overload.Besides, who has more knowledge about specific project elements are important to promote collaboration.AIMS: The K a , K s , K c , and K p models capture the evolution of the developers' knowledge about software project elements such as artifacts, tasks, similar tasks, and the whole software project.These models represent not only the knowledge developers have about these elements but also capture how this knowledge decreases over time based on forgetting and relearning functions.EVALUATION: An experimental study analyzed some developers' interactions on artifacts for the purpose of predicting the evolution of developers' knowledge in six software projects.The results show that the developers' rankings by performed tasks and by our models have 72% or more of similarity.CONCLUSION: Our models can capture and assess the evolution of the developers' knowledge and help to identify which developers have more knowledge about specific elements of software projects.
Source-Word Decomposition for Neural Machine Translation

End-to-end neural machine translation does not require us to have specialized knowledge of investigated language pairs in building an effective system. On the other hand, feature engineering proves to be vital in other artificial intelligence fields, such as speech recognition and computer vision. Inspired by works in those fields, in this paper, we propose a novel feature-based translation model by modifying the state-of-the-art transformer model. Specifically, the encoder of the modified transformer model takes input combinations of linguistic features comprising of lemma, dependency label, part-of-speech tag, and morphological label instead of source words. The experiment results for the Russian-Vietnamese language pair show that the proposed feature-based transformer model improves over the strongest baseline transformer translation model by impressive 4.83 BLEU. In addition, experiment analysis reveals that human judgment on the translation results strongly confirms machine judgment. Our model could be useful in building translation systems translating from a highly inflectional language into a noninflectional language.
Impact of Information and Communication Technology (ICT) Investment on different Components of Human Development in Developing Countries

ICT investment is the key factor of human development in developing countries. Human development has three main components such as GDP, health and education. The current study explores the impact of ICT investment on the components of human development in 67 selected developing countries. Selected developing countries are divided into four panels on the basis of income as lower, lower middle, upper middle and high income countries. Data were collected from World Bank, ITU and Word Information Technology and Services Alliances website for the period of 2000 to 2018. Pesaran, Friedman and Frees CSD tests confirmed the presence of cross-sectional dependency in the variables and consequently, CIPS second generation unit root test were used for stationarity. Kao and Pedroni test and ARDL model were employed to check the long-run cointegration and regression analysis respectively. The regression results showed the mixed findings in different panels. The results explored that ICT investment have different impacts on the components of human development in four panels of the developing countries. Communication investment, software and hardware investment have positive impact on the components of human development in all panels. It is recommended that government should focus on ICT investment to increase GDP, level of education index, life expectancy index and HDI. Keywords: ICT Investment, Human Development, Developing Countries, Panel Data.
Fact-Enhanced Synthetic News Generation

The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a novel generation method FACTGEN to generate high-quality news content. The majority of existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy. To address these issues, FACTGEN retrieves external facts to enrich the output and reconstructs the input claim from the generated content to improve the consistency among the input and the output. Experiment results on real-world datasets demonstrate that the generated news contents of FACTGEN are consistent and contain rich facts. We also discuss an effective defending technique to identify these synthetic news pieces if FACTGEN was used to generate fake news.
Client-side rogue access-point detection using a simple walking strategy and round-trip time analysis

Abstract Traditional rogue access-point (AP) detection mechanisms are employed in network administration to protect network infrastructure and organization; however, these mechanisms do not protect end users from connecting to a rogue-AP. In this paper, a rogue-AP detection technique on the mobile-user side is proposed. By using a simple method involving walking, the round-trip time (RTT) and the modulation and coding scheme values are obtained, and a more accurate transmission rate for particular RTT values is thereby calculated. Further, the cleansed data are classified using the k-means method and the cumulative distribution function for the detection process. The results demonstrate that a rogue-AP can be detected with an F-measure value of up to 0.9. In the future, the proposed algorithm can be implemented as an application installed on mobile devices so that nontechnical users can detect rogue-APs.
Image Annotation via Reconstitution Graph Learning Model

With great developments of computing technologies and data mining methods, image annotation has attracted much attraction in smart agriculture. However, the semantic gap between labels and images poses great challenges on image annotation in agriculture, due to the label imbalance and difficulties in understanding obscure relationships of images and labels. In this paper, an image annotation method based on graph learning is proposed to accurately annotate images. Specifically, inspired by nearest neighbors, the semantic neighbor graph is introduced to generate preannotation, balancing unbalanced labels. Then, the correlations between labels and images are modeled by the random dot product graph, to deeply mine semantics. Finally, we perform experiments on two image sets. The experimental results show that our method is much better than the previous method, which verifies the effectiveness of our model and the proposed method.
A generalized quality assessment method for natural and screen content images

A generalized objective quality assessment method is proposed for natural images and screen content images. Since natural images and screen content images have different statistical properties, the modelling of a generalized quality assessment method that works for both types of images is complicated because some properties of natural images and screen content images are conflicting to one another. The proposed method assesses the perceptual quality of an image based on edge magnitude and direction. In this method, an image is first separated into regions with high and low gradients. Gradient is used due to the small perceptual span of the human visual system for textual content. For high gradient regions, small kernel size of Prewitt operators is used to obtain the gradient magnitude and direction. Correspondingly, bigger kernel size of Prewitt operators is utilized for low gradient regions. Visual quality indices are computed from both regions and pooled to obtain the final quality index. From the performance comparison, it is shown that the proposed method could assess the perceived quality of natural images and screen content images with high accuracy.
Fast and Simple Modular Subset Sum

We revisit the Subset Sum problem over the finite cyclic group ℤm for some given integer m. A series of recent works has provided near-optimal algorithms for this problem under the Strong Exponential Time Hypothesis. Koiliaris and Xu (SODA'17, TALG'19) gave a deterministic algorithm running in time , which was later improved to O(m log7 m) randomized time by Axiotis et al. (SODA'19).In this work, we present two simple algorithms for the Modular Subset Sum problem running in near-linear time in m, both efficiently implementing Bellman's iteration over ℤm. The first one is a randomized algorithm running in time O(m log2 m), that is based solely on rolling hash and an elementary data-structure for prefix sums; to illustrate its simplicity we provide a short and efficient implementation of the algorithm in Python. Our second solution is a deterministic algorithm running in time ℤ(m polylog m), that uses dynamic data structures for string manipulation.We further show that the techniques developed in this work can also lead to simple algorithms for the All Pairs Non-Decreasing Paths Problem (APNP) on undirected graphs, matching the near-optimal running time of provided in the recent work of Duan et al. (ICALP'19).
Feature-ranking-based ensemble classifiers for survivability prediction of intensive care unit patients using lab test data

Clinical decision support systems (CDSSs) have received increasing research attention in recent years because they can improve the quality, safety, efficiency, and effectiveness of healthcare. A CDSS combined with advanced data analytics is more accurate and efficient than traditional systems. In this domain, survival or deterioration prediction of critical care patients, e.g., intensive care unit (ICU) patients, is an active research area. Early deterioration prediction can help healthcare providers in providing efficient and effective patient care. Research in this field is primarily based on vital signs. However, very few studies have investigated survival prediction using lab test data. Although some studies have made advancements in this field, accuracy remains insufficient. Thus, this study aims to improve the accuracy and efficiency of survival prediction for ICU patients. We propose a feature-ranking-based ensemble of classifiers for survival prediction of ICU patients using only lab test data. In the proposed method, features are evaluated first, and subsets of useful features are selected. Subsequently, training data with the selected features are clustered using a feature vector compaction (FVC) technique. Finally, ensemble classifier models are trained. Extensive experiments with over 3000 different settings on six ICU patient datasets were performed to evaluate the efficacy of the proposed method. The proposed technique achieves weighted average F1 score (Fwa) as high as 82.6% with support vector machine classifier when feature ranking is used with a combination of vertical and horizontal grouping-based FVC. All experimental results demonstrate that this technique outperforms existing methods, with the Fwa score difference being as high as 4.5%.
Preference-Aware Dynamic Bitrate Adaptation for Mobile Short-Form Video Feed Streaming

Short-form video feed becomes increasingly popular among younger generations, where mobile users watch videos of a few seconds one-by-one in a session or a pre-defined list. The common solution to improve the quality of experience (QoE) for short-video feed is to implement dynamic adaptive streaming strategies to decide bitrate for each video. However, legacy bitrate adaptation strategies fail to differentiate the weights of videos in the list according to the user preferences, leading to the bandwidth waste on downloading those video content skipped immediately by the users without being displayed. In this paper, we propose RecDASH, which consists of an attention-based user modeling module leveraging advanced recommendation algorithms and a reinforcement learning (RL) based bitrate adaptation module. Specifically, we use gated recurrent unit (GRU) network with attention mechanism to encode the session into a representation vector. Then, RL module combines the representation of session and other observations within playback, and yields the appropriate bitrate for the next short-form video for optimizing a given QoE objective. The low space and time complexity of the model enables it to be easily deployed on mobile devices. Trace-driven emulations verify the efficiency of RecDASH compared to several state-of-the-art streaming strategies with at least 5%-15% improvement on the video quality under various QoE objectives.
Classification of the User's Intent Detection in Ecommerce systems – Survey and Recommendations

The personalized experience gets more and more attention these days.Many e-commerce businesses are looking for methods to deliver personalized service.Consumers are expecting, if not demanding, highly personalized experiences.Moreover, customers are typically willing to spend more when they receive such a custom-tailored service.A prerequisite to provide a genuinely personalized experience is to understand the customer.Intent detection is a new and challenging approach in modern e-commerce to understand the customer.We find that various aspects of customer intent detection can be tackled by leveraging tremendous recent recommendation systems' progress.In this work, we review existing works from different domains that can be re-used for customer intent detection in the e-commerce.Even though many methods are used, there is no comparison of available approaches.Based on a review of nearly 100 articles from 2015 until 2019, we propose a categorization of types of intent detection, personalization context, building a customer profile, and dynamic changes in user interests handling.We also summarize existing methods from applicability in the e-commerce domain, including the aspect of the General Data Protection Regulation requirements.The paper aims at the classification of applied techniques and highlights their advantages and disadvantages.
Proof of Work

The primary goal of this chapter is to clearly explain how blocks are mined in a blockchain. Indirectly, this explains how new currency comes into being, as well as how disparate people forming a network can all reach consensus (agree on the state of the blockchain). And so, you’ll leave this chapter with a practical understanding of Proof of Work—the protocol which achieves this.
The Accuracy and Shortcomings of Google Translate Translating English Sentences to Indonesian

Google Translate is a free and practical online translation service that allows millions of people around the globe to translate words, phrases, sentences, and paragraphs into an intended target language. However, in 2015, some Google Translate users in Indonesia, filed complaints, asserting that the machine was often inaccurate, speculating that it could only translate languages at the micro-level of words and phrases, rather than complete sentences or paragraphs. This research works to examine the accuracy as well as the shortcomings of Google Translate, in the context of English to Indonesian translations, in order to critically engage the complaints made by Google users. For the purpose of this study, 80 English sentences were translated using Google Translate and assessed for accuracy using a table adapted from Memsource criteria. Both the original sentences and their translated versions were analyzed using a sentence pair matrix to determine the machine’s failings and areas for improvement. The results challenged those initial speculations which suggested Google Translate is only effective with words and phrases. On the contrary, Memsource proved to be a useful tool in demonstrating a reasonable level of accuracy, accurately translating 60.37% of Indonesian-English sentences and vice versa.
Community Understanding of the Importance of Social Distancing Using Sentiment Analysis in Twitter

The government may use social media, such as Twitter, to socialize a policy or a program to society.We may predict whether a program is successful or not by analyzing the sentiment of societies towards such program or communities through their tweets.The latest program of Indonesia's government during the COVID-19 pandemic is to make people do social distancing.It is socialized using the hashtag of stay at home appeal (#dirumahaja).The objective of this study is to analyze the understanding of societies regarding this program through people's tweet.We compared two classification algorithms (Naïve Bayes and Random Forest), using tokenization and unigram features to build classification model of tweet sentiment.The tweets that included some hashtags regarding social distancing program, were collected with 5101 tweets in total.The highest accuracy is obtained using the Random Forest algorithm and term weighting feature, which yielded 95.98%.From the model we found that the number of positive sentiments is greater than the negative sentiment.Which can be concluded that the societies are understand and agree to the social distancing program.
Impact of Foreign Direct Investment on Economic Growth in Ethiopia

The aim of the study is to investigate the Impact of Foreign Direct Investment on economic growth in Ethiopia by using annual time series data from the periods of 1982 to 2018.To achieve this objective the Descriptive and Econometric analysis were processed.The Ordinary Least square (OLS) method approach was employed to assess the model in the long run.The empirical results points out that as foreign direct investment has a positive and significant impact on the economic growth of Ethiopia.Beside this other variables such as gross capital formation, gross domestic saving and infrastructural level also have positive and significant contribution on the economic growth and enhance the growth of the country.Error-correction model (ECM) has been used to support the existence of a stable long-term relationship and confirm a deviation from the longterm equilibrium.The finding of error-correction model revealed that there was a deviation of the actual growth of the country from its equilibrium value that eliminated every years and indicates a short run deviation of growth from the long run equilibrium.In doing so, the Diagnostic and Unit root tests were conducted to guard the model from spurious results.Based on the finding of the study the researcher recommended that all concerning bodies to be utilized properly and wisely the opportunity of foreign direct investment out comes as it continues to increase the economic growth and motivate them as they enter in to the country for investment.
SumTitles: a Summarization Dataset with Low Extractiveness

The existing dialogue summarization corpora are significantly extractive.We introduce a methodology for dataset extractiveness evaluation and present a new low-extractive corpus of movie dialogues for abstractive text summarization along with baseline evaluation.The corpus contains 153k dialogues and consists of three parts: 1) automatically aligned subtitles, 2) automatically aligned scenes from scripts, and 3) manually aligned scenes from scripts.We also present an alignment algorithm which we use to construct the corpus.
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training

We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by calculating the matching score based on learned features, leading to insufficient model reasoning ability. In this paper, we propose a graph- reasoning network (GRN) to address the problem. GRN first conducts pre-training based on ALBERT using next utterance prediction and utterance order prediction tasks specifically devised for response selection. These two customized pre-training tasks can endow our model with the ability of capturing semantical and chronological dependency between utterances. We then fine-tune the model on an integrated network with sequence reasoning and graph reasoning structures. The sequence reasoning module conducts inference based on the highly summarized context vector of utterance-response pairs from the global perspective. The graph reasoning module conducts the reasoning on the utterance-level graph neural network from the local perspective. Experiments on two conversational reasoning datasets show that our model can dramatically outperform the strong baseline methods and can achieve performance which is close to human-level.