Miracle-dz/UniFAR-FTUs huggingface dataset:
Video Retrieval with Similarity-Preserving Deep Temporal Hashing

Despite the fact that remarkable progress has been made in recent years, Content-based Video Retrieval (CBVR) is still an appealing research topic due to increasing search demands in the Internet era of big data. This article aims to explore an efficient CBVR system by discriminately hashing videos into short binary codes. Existing video hashing methods usually encounter two weaknesses originating from the following sources: (1) Most works adopt the separated stages method or the frame-pooling based end-to-end architecture. However, the spatial-temporal properties of videos cannot be fully explored or kept well in the follow-up hashing step. (2) Discriminative learning based on pairwise or triplet constraints often suffers from slow convergence and poor local optimization, mainly because of the limited samples for each update. To alleviate these problems, we propose an end-to-end video retrieval framework called the Similarity-Preserving Deep Temporal Hashing (SPDTH) network. Specifically, we equip the model with the ability to capture spatial-temporal properties of videos and to generate binary codes by stacked Gated Recurrent Units (GRUs). It unifies video temporal modeling and learning to hash into one step to allow for maximum retention of information. We also introduce a deep metric learning objective called ℓ2All_loss for network training by preserving intra-class similarity and inter-class separability, and a quantization loss between the real-valued outputs and the binary codes is minimized. Extensive experiments on several challenging datasets demonstrate that SPDTH can consistently outperform state-of-the-art methods.
Adapt, Explain, Engage—A Study on How Social Robots Can Scaffold Second-language Learning of Children

Social robots are increasingly applied to support children’s learning, but how a robot can foster (or may hinder) learning is still not fully clear. One technique used by teachers is scaffolding, temporarily assisting learners to achieve new skills or levels of understanding they would not reach on their own. We ask if and how a social robot can be utilized to scaffold second-language learning of children at kindergarten age (4--7 years). Specifically, we explore an adapt-and-explain scaffolding strategy in which a robot acts as a peer-like tutor who dynamically adapts its behavior or the learning tasks to the cognitive and affective state of the child, and provides verbal explanations of these adaptations. An evaluation study with 40 children shows that children benefit from the learning adaptation and that the explanations have a positive effect especially for slower learners. Further, in 76% of all cases the robot managed to “re-engage” children who started to disengage from the learning interaction, helping them to achieve an overall higher learning gain. These findings demonstrate that a social robot equipped with suitable scaffolding mechanisms can increase engagement and learning, especially when being adaptive to the individual behavior and states of a child learner.
ReVibe

Besides passive sensing, ecological momentary assessments (EMAs) are one of the primary methods to collect in-the-moment data in ubiquitous computing and mobile health. While EMAs have the advantage of low recall bias, a disadvantage is that they frequently interrupt the user and thus long-term adherence is generally poor. In this paper, we propose a less-disruptive self-reporting method, "assisted recall," in which in the evening individuals are asked to answer questions concerning a moment from earlier in the day assisted by contextual information such as location, physical activity, and ambient sounds collected around the moment to be recalled. Such contextual information is automatically collected from phone sensor data, so that self-reporting does not require devices other than a smartphone. We hypothesized that providing assistance based on such automatically collected contextual information would increase recall accuracy (i.e., if recall responses for a moment match the EMA responses at the same moment) as compared to no assistance, and we hypothesized that the overall completion rate of evening recalls (assisted or not) would be higher than for in-the-moment EMAs. We conducted a two-week study (N=54) where participants completed recalls and EMAs each day. We found that providing assistance via contextual information increased recall accuracy by 5.6% (p = 0.032) and the overall recall completion rate was on average 27.8% (p < 0.001) higher than that of EMAs.
My Mom was Getting this Popup

Security and privacy pose a serious barrier to the use of mobile technology by older adults. While support from family and friends is known to be an effective enabler in older adults' technology adoption, we know very little about the family members' motivations for providing help, the context, and the process in which they provide it. To bridge this gap, we have conducted a mixed method study, qualitatively analyzing the helpers' assistance stories and quantitatively estimating the factors that affect helpers' willingness to offer assistance to older relatives regarding mobile security and privacy problems. Our findings point to the potential for helping older relatives, i.e., people are more willing to help and guide them than other social groups. Furthermore, we show that familiarity with an older relative's preferences is essential in providing meaningful support. We discuss our findings in the context of developing a theory of collective efficacy for security and privacy and new collaborative technologies that can reduce the barriers to social help.
ER-Rhythm

The locomotor-respiratory coupling (LRC) ratio of a person doing exercise is an important parameter to reflect the exercise safety and effectiveness. Existing approaches that can measure LRC either rely on specialized and costly devices or use heavy sensors, bringing much inconvenience to people during exercise. To overcome these limitations, we propose ER-Rhythm using low-cost and lightweight RFID tags attached on the human body to simultaneously extract and couple the exercise and respiration rhythm for LRC estimation. ER-Rhythm captures exercise locomotion rhythm from the signals of the tags on limbs. However, extracting respiration rhythm from the signals of the tags on the chest during exercise is a challenging task because the minute respiration movement can be overwhelmed by the large torso movement. To address this challenge, we first leverage the unique characteristic of human respiratory mechanism to measure the chest movement while breathing, and then perform dedicated signal fusion of multiple tags interrogated by a pair of antennas to remove the torso movement effect. In addition, we take advantage of the multi-path effect of RF signals to reduce the number of needed antennas for respiration pattern extraction to save the system cost. To couple the exercise and respiration rhythm, we adopt a correlation-based approach to facilitate LRC estimation. The experimental results show that LRC can be estimated accurately up to 92% -- 95% of the time.
Video Enhancement & Suspicious Object Detection In Low Quality Video Frames

To develop the real world computer vision system, detection of moving objects in video images is very important. The automatic detection of moving objects in video images is very important. The automatic detection of moving objects in monitoring system needs efficient algorithms. The common method is simple background subtraction i.e. to subtract current image from background. But it can’t detect the difference when brightness difference between moving objects and background is small. The other approach is to use some algorithms such as color based subtraction technique but the costs are very high and have problem in stability. Here a method is proposed to detect moving objects using difference of two consecutive frames. The objective is to provide a software that can be used on a pc for performing tracking along with video enhancement using bilinear interpolation. The program is able to track moving objects and it is structured as different blocks working together. Initially the spatial resolution and the contrast of the extracted frames of the video sequence are enhanced. The position of the object is now marked manually so as to obtain the “Region of Interest”. The algorithm is implemented in MATLAB and the results demonstrate that both the accuracy and processing speed are very promising. Furthermore, the algorithm is robust to the changes of lighting condition and camera noise. The algorithm can be used in video based applications such as automatic video surveillance. KeywordsVideo, Frames extraction, Image Enhancement, Object Tracking and detection ,Suspicious objects --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 25-04-2018 Date of acceptance: 14-05-2018 -------------------------------------------------------------------------------------------------------------------------------------
Saffron: Adaptive Grammar-based Fuzzing for Worst-Case Analysis

Fuzz testing has been gaining ground recently with substantial e orts devoted to the area. Typically, fuzzers take a set of seed inputs and leverage random mutations to continually improve the inputs with respect to a cost, e.g. program code coverage, to discover vulnerabilities or bugs. Following this methodology, fuzzers are very good at generating unstructured inputs that achieve high coverage. However fuzzers are less e ective when the inputs are structured, say they conform to an input grammar. Due to the nature of random mutations, the overwhelming abundance of inputs generated by this common fuzzing practice often adversely hinders the effectiveness and efficiency of fuzzers on grammar-aware applications. The problem of testing becomes even harder, when the goal is not only to achieve increased code coverage, but also to nd complex vulnerabilities related to other cost measures, say high resource consumption in an application. We propose Saffron an adaptive grammar-based fuzzing approach to effectively and efficiently generate inputs that expose expensive executions in programs. Saffron takes as input a user-provided grammar, which describes the input space of the program under analysis, and uses it to generate test inputs. Saffron assumes that the grammar description is approximate since precisely describing the input program space is often difficult as a program may accept unintended inputs due to e.g., errors in parsing. Yet these inputs may reveal worst-case complexity vulnerabilities. The novelty of Saffron is then twofold: (1) Given the user-provided grammar, Saffron attempts to discover whether the program accepts unexpected inputs outside of the provided grammar, and if so, it repairs the grammar via grammar mutations. The repaired grammar serves as a speci cation of the actual inputs accepted by the application. (2) Based on the re ned grammar, it generates concrete test inputs. It starts by treating every production rule in the grammar with equal probability of being used for generating concrete inputs. It then adaptively re nes the probabilities along the way by increasing the probabilities for rules that have been used to generate inputs that improve a cost, e.g., code coverage or arbitrary user-de ned cost. Evaluation results show that Saffron signi cantly outperforms state-of-the-art baselines.
Tiling-Based Programming Model for Structured Grids on GPU Clusters

Currently, more than 25% of supercomputers employ GPUs due to their massively parallel and power-efficient architectures. However, programming GPUs efficiently in a large scale system is a demanding task not only for computational scientists but also for programming experts as multi-GPU programming requires managing distinct address spaces, generating GPU-specific code and handling inter-device communication. To ease the programming effort, we propose a tiling-based high-level GPU programming model for structured grid problems. The model abstracts data decomposition, memory management and generation of GPU specific code, and hides all types of data transfer overheads. We demonstrate the effectiveness of the programming model on a heat simulation and a real-life cardiac modeling on a single GPU, on a single node with multiple-GPUs and multiple-nodes with multiple-GPUs. We also present performance comparisons under different hardware and software configurations. The results show that the programming model successfully overlaps communication and provides good speedup on 192 GPUs.
A CNN based approach to Phrase-Labelling through classification of N-Grams

Modern approaches address the task of Phrase-labelling within any input sentence (eg: NER, Chunking etc.) as a variant of word-tagging problem. These approaches extract the desired phrases as word-sequences which are mapped to specific tag-sequences (with Begin, Intermediate and End tag-types). However we argue that basic nature of Phrase-labelling is not temporal but spatial in nature. Thus we propose and test the hypothesis that a CNN based model that directly extracts labelled n-grams from the input sentence would outperform standard RNN based model.
A Review on OLAP Technologies Applied to Information Networks

Many real systems produce network data or highly interconnected data, which can be called information networks. These information networks form a critical component in modern information infrastructure, constituting a large graph data volume. The analysis of information network data covers several technological areas, among them OLAP technologies. OLAP is a technology that enables multi-dimensional and multi-level analysis on a large volume of data, providing aggregated data visualizations with different perspectives. This article presents a literature review on the main applications of OLAP technology in the analysis of information network data. To achieve such goal, it shows a systematic review to list the works that apply OLAP technologies in graph data. It defines seven comparison criteria (Materialization, Network, Selection, Aggregation, Model, OLAP Operations, Analytics) to qualify the works found based on their functionalities. The works are analyzed according to each criterion and discussed to identify trends and challenges in the application of OLAP in the information network.
AppMoD

The rapid adoption of Smartphone devices has caused increasing security and privacy risks and breaches. Catching up with ever-evolving contemporary smartphone technology challenges leads older adults (aged 50+) to reduce or to abandon their use of mobile technology. To tackle this problem, we present AppMoD, a community-based approach that allows delegation of security and privacy decisions a trusted social connection, such as a family member or a close friend. The trusted social connection can assist in the appropriate decision or make it on behalf of the user. We implement the approach as an Android app and describe the results of three user studies (n=50 altogether), in which pairs of older adults and family members used the app in a controlled experiment. Using app anomalies as an ongoing case study, we show how delegation improves the accuracy of decisions made by older adults. Also, we show how combining decision-delegation with crowdsourcing can enhance the advice given and improve the decision-making process. Our results suggest that a community-based approach can improve the state of mobile security and privacy.
Investigating Users' Preferences and Expectations for Always-Listening Voice Assistants

Many consumers now rely on different forms of voice assistants, both stand-alone devices and those built into smartphones. Currently, these systems react to specific wake-words, such as "Alexa," "Siri," or "Ok Google. " However, with advancements in natural language processing, the next generation of voice assistants could instead always listen to the acoustic environment and proactively provide services and recommendations based on conversations without being explicitly invoked. We refer to such devices as "always listening voice assistants" and explore expectations around their potential use. In this paper, we report on a 178-participant survey investigating the potential services people anticipate from such a device and how they feel about sharing their data for these purposes. Our findings reveal that participants can anticipate a wide range of services pertaining to a conversation; however, most of the services are very similar to those that existing voice assistants currently provide with explicit commands. Participants are more likely to consent to share a conversation when they do not find it sensitive, they are comfortable with the service and find it beneficial, and when they already own a stand-alone voice assistant. Based on our findings we discuss the privacy challenges in designing an always-listening voice assistant.
Complete Shadow Symbolic Execution with Java PathFinder

Regression testing ensures the correctness of the software during its evolution, with special attention on the absence of unintended side-e ects that might be introduced by changes. However, the manual creation of regression test cases, which expose divergent behavior, needs a lot of e ort. A solution is the idea of shadow symbolic execution, which takes a uni ed version of the old and the new programs and performs symbolic execution guided by concrete values to explore the changed behavior. In this work, we adapt the idea of shadow symbolic execution (SSE) and combine complete/standard symbolic execution with the idea of four-way forking to expose diverging behavior. There- fore, our approach attempts to comprehensively test the new be- haviors introduced by a change. We implemented our approach in the tool ShadowJPF+, which performs complete shadow sym- bolic execution on Java bytecode. It is an extension of the tool ShadowJPF, which is based on Symbolic PathFinder. We applied our tool on 79 examples, for which it was able to reveal more di- verging behaviors than common shadow symbolic execution. Ad- ditionally, the approach has been applied on a real-world patch for the Joda-Time library, for which it successfully generated test cases that expose a regression error.
Method summaries for JPF

Java Path nder (JPF) is a virtual machine executing Java byte- code that is able to perform model checking using backtracking execution. Due to backtracking, parts of a program may be ex- ecuted multiple times during model checking. Hence, we explore whether method summaries can be used to make JPF's model checking more efficient. We present the design and implementa- tion of dynamically generated summaries as an extension of JPF. While our summaries incur an overhead that outweighs the bene- ts in most cases, the approach shows promise in certain cases, in particular when stateless model checking is used. We also provide some results related to cases when our summaries are applicable that could provide guidance for future work within this eld.
Local Overlapping Community Detection

Local community detection refers to finding the community that contains the given node based on local information, which becomes very meaningful when global information about the network is unavailable or expensive to acquire. Most studies on local community detection focus on finding non-overlapping communities. However, many real-world networks contain overlapping communities like social networks. Given an overlapping node that belongs to multiple communities, the problem is to find communities to which it belongs according to local information. We propose a framework for local overlapping community detection. The framework has three steps. First, find nodes in multiple communities to which the given node belongs. Second, select representative nodes from nodes obtained above, which tends to be in different communities. Third, discover the communities to which these representative nodes belong. In addition, to demonstrate the effectiveness of the framework, we implement six versions of this framework. Experimental results demonstrate that the six implementation versions outperform the other algorithms.
A Bloom Filter Based Algorithm for Routing in Intermittently Connected Ad Hoc Networks

In this paper we present a new protocol for routing in intermittently connected mobile ad hoc networks. The proposed protocol establishes a single probabilistic gradient that can be followed to reach any destination in the network. In order to establish this gradient, nodes periodically exchange counting Bloom filters that condense the topological information known by nodes. Experimental results based on extensive detailed simulations show that the proposed protocol clearly outperforms the epidemic routing protocol by delivering up to three times more data packets.
A Hybrid Whale Optimization Algorithm with Adaptive Spiral for Terrorism Prediction (The Case of Egypt)

This paper proposes a hybrid prediction classification algorithm based ona modification in therecently proposed whale optimization algorithm (WOA), which mimics the social behavior of humpback whales.The modified WOA proposed to enhance the exploitation capability of the original WOA by introducing a modifiedspiral to simulate the helix-shaped movement of humpback whales.The proposed hybridprediction modified WO algorithm is used to predict the terrorist group responsible of terrorist attack(s) on Egypt based on the prediction by the classification algorithm combined with the wrapper feature selection based approach to selectthe optimal feature subset for the classification purposes. The proposed algorithm has been tested on real terrorism data of Egypt, as well as the results have been compared with other well-known nature-inspired algorithms as Moth Flame Optimization algorithm (MFO) and the two modified versions (MFO-2, MFO3),Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A set of statistical assessment indicators are used to evaluate and compare between the obtained results which proved that the proposed hybrid predictionmodified version of WOA provides very promising and competitive performance as well as achievesan advance over the original WOA algorithm with high stability over other searching methods.
Preparation and Characterization of Methotrexate Loaded Polymeric Nanoparticles by Nanoprecipitation Technique

The aim of this study was to develop and characterize methotrexate-loaded polymeric nanoparticles by nanoprecipitation technique. Eudragit® S 100 and ethyl cellulose were employed to develop methotrexate-loaded nanoparticles by nanoprecipitation technique. Six different formulations (f1, f2, f3, f4, f5 and f6) were prepared with each polymer by varying the drug to polymer ratios (1:1, 1:1.5, 1:2, 1:3, 1.5:1 and 2:1). Dimethyl sulfoxide (DMSO) was used as a solvent and Tween® 20 as a surfactant. Among the six formulations of polymeric nanoparticles prepared by nanoprecipitation technique, F4 formulation was considered as a best formulation with each polymer. Based on comparison results of mean particle diameter, zeta potential, drug content and entrapment efficiency, Eudragit® S 100 was considered to be the most suitable polymer for preparation of methotrexate-loaded nanoparticles by nanoprecipitation technique. Based upon the evaluation studies, the best formulation was characterized for scanning electron microscopy (SEM), particle size, zeta potential and anti-cancer activity in MCF-7 cell line by MTT assay.