OpenRaiser/Intern-Atlas huggingface dataset:
Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing
Venue: arXiv cs.AR 2018
Year: 2018

Convolutional neural networks (CNNs) are one of the most successful machine learning techniques for image, voice and video processing. CNNs require large amounts of processing capacity and memory bandwidth. Hardware accelerators have been proposed for CNNs which typically contain large numbers of multiply-accumulate (MAC) units, the multipliers of which are large in an integrated circuit (IC) gate count and power consumption. "Weight sharing" accelerators have been proposed where the full range of weight values in a trained CNN are compressed and put into bins and the bin index used to access the weight-shared value. We reduce power and area of the CNN by implementing parallel accumulate shared MAC (PASM) in a weight shared CNN. PASM re-architects the MAC to instead count the frequency of each weight and place it in a bin. The accumulated value is computed in a subsequent multiply phase, significantly reducing gate count and power consumption of the CNN. In this paper, we implement PASM in a weight-shared CNN convolution hardware accelerator and analyze its effectiveness. Experiments show that for a clock speed 1GHz implemented on a 45nm ASIC process our approach results in fewer gates, smaller logic, and reduced power with only a slight increase in latency. We also show that the same weight-shared-with-PASM CNN accelerator can be implemented in resource-constrained FPGAs, where the FPGA has limited numbers of digital signal processor (DSP) units to accelerate the MAC operations.
Energy-efficient Deployment of Relay Nodes in Wireless Sensor Networks using Evolutionary Techniques
Venue: arXiv cs.ET 2018
Year: 2018

Random deployment of sensor nodes is susceptible to initial communication hole, even when the network is densely populated. However, eliminating holes using structural deployment poses its difficulties. In either case, the resulting coverage holes can degrade overall network performance and lifetime. Many solutions utilizing Relay Nodes (RNs) have been proposed to alleviate this problem. In this regard, one of the recent solutions proposed using Artificial Bee Colony (ABC) to deploy RNs. This paper proposes RN deployment using two other evolutionary techniques - Gravitational Search Algorithm (GSA) and Differential Evolution (DE) and compares them with existing solution that uses ABC. These popular optimization tools are deployed to optimize the positions of relay nodes for lifetime maximization. Proposed algorithms guarantee satisfactory RNs utilization while maintaining desired connectivity level. It is shown that DE-based deployment improves the network lifetime better than other optimization heuristics considered.
The Reification of an Incorrect and Inappropriate Spreadsheet Model
Venue: arXiv cs.HC 2018
Year: 2018

Once information is loaded into a spreadsheet, it acquires properties that it may not deserve. These properties include believability, correctness, appropriateness, concreteness, integrity, tangibility, objectivity and authority. The information becomes reified. We describe a case study through which we were able to observe at close hand the reification of a demonstrably incorrect and inappropriate spreadsheet model within a small non profit organisation.
The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval
Venue: arXiv cs.CL 2018
Year: 2018

Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages. We propose to treat these ideograms as a new modality in their own right, distinct in their semantic structure from both the text in which they are often embedded as well as the images which they resemble. As a new modality, emoji present rich novel possibilities for representation and interaction. In this paper, we explore the challenges that arise naturally from considering the emoji modality through the lens of multimedia research. Specifically, the ways in which emoji can be related to other common modalities such as text and images. To do so, we first present a large scale dataset of real-world emoji usage collected from Twitter. This dataset contains examples of both text-emoji and image-emoji relationships. We present baseline results on the challenge of predicting emoji from both text and images, using state-of-the-art neural networks. Further, we offer a first consideration into the problem of how to account for new, unseen emoji - a relevant issue as the emoji vocabulary continues to expand on a yearly basis. Finally, we present results for multimedia retrieval using emoji as queries.
A Delay-Tolerant Payment Scheme Based on the Ethereum Blockchain
Venue: arXiv cs.CY 2018
Year: 2018

Banking as an essential service can be hard to access in remote, rural regions where the network connectivity is intermittent. Although micro-banking has been made possible by SMS or USSD messages in some places, their security flaws and session-based nature prevent them from a wider adoption. Global level cryptocurrencies enable low-cost, secure and pervasive money transferring among distributed peers, but are still limited in their ability to reach more people in remote communities. We proposed to take advantage of the delay-tolerant nature of blockchains to deliver banking services to remote communities that only connect to the broader Internet intermittently. Using a base station that offers connectivity within the local area, regular transaction processing is solely handled by blockchain miners. The bank only joins to process currency exchange requests, reward miners and track user balances when the connection is available. By distributing the verification and storage tasks among peers, our system design saves on the overall deployment and operational costs without sacrificing the reliability and trustwor- thiness. Through theoretical and empirical analysis, we provided insights to system design, tested its robustness against network disturbances, and demonstrated the feasibility of implementation on off-the-shelf computers and mobile devices.
Haptics of Screwing and Unscrewing for its Application in Smart Factories for Disassembly
Venue: arXiv cs.RO 2018
Year: 2018

Reconstruction of skilled humans sensation and control system often leads to a development of robust control for the robots. We are developing an unscrewing robot for the automated disassembly which requires a comprehensive control system, but unscrewing experiments with robots are often limited to several conditions. On the contrary, humans typically have a broad range of screwing experiences and sensations throughout their lives, and we conducted an experiment to find these haptic patterns. Results show that people apply axial force to the screws to avoid screwdriver slippage (cam-outs), which is one of the key problems during screwing and unscrewing, and this axial force is proportional to the torque which is required for screwing. We have found that type of the screw head influences the amount of axial force applied. Using this knowledge an unscrewing robot for the smart disassembly factory RecyBot is developed, and experiments confirm the optimality of the strategy, used by humans. Finally, a methodology for robust unscrewing algorithm design is presented as a generalization of the findings. It can seriously speed up the development of the screwing and unscrewing robots and tools.
Robust Designs of Beamforming and Power Splitting for Distributed Antenna Systems with Wireless Energy Harvesting
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate a multiuser distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information (CSI). In this system, a distributed antenna port with multiple antennas supports a set of mobile stations who can decode information and harvest energy simultaneously via a power splitter. To design robust transmit beamforming vectors and the power splitting (PS) factors in the presence of CSI errors, we maximize the average worst-case signal-to-interference-plus- noise ratio (SINR) while achieving individual energy harvesting constraint for each mobile station. First, we develop an efficient algorithm to convert the max-min SINR problem to a set of "dual" min-max power balancing problems. Then, motivated by the penalty function method, an iterative algorithm based on semi-definite programming (SDP) is proposed to achieve a local optimal rank-one solution. Also, to reduce the computational complexity, we present another iterative scheme based on the Lagrangian method and the successive convex approximation (SCA) technique to yield a suboptimal solution. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.
A family of OWA operators based on Faulhaber's formulas
Venue: arXiv cs.AI 2018
Year: 2018

In this paper we develop a new family of Ordered Weighted Averaging (OWA) operators. Weight vector is obtained from a desired orness of the operator. Using Faulhaber's formulas we obtain direct and simple expressions for the weight vector without any iteration loop. With the exception of one weight, the remaining follow a straight line relation. As a result, a fast and robust algorithm is developed. The resulting weight vector is suboptimal according with the Maximum Entropy criterion, but it is very close to the optimal. Comparisons are done with other procedures.
Percolation for D2D Networks on Street Systems
Venue: arXiv cs.NI 2018
Year: 2018

We study fundamental characteristics for the connectivity of multi-hop D2D networks. Devices are randomly distributed on street systems and are able to communicate with each other whenever their separation is smaller than some connectivity threshold. We model the street systems as Poisson-Voronoi or Poisson-Delaunay tessellations with varying street lengths. We interpret the existence of adequate D2D connectivity as percolation of the underlying random graph. We derive and compare approximations for the critical device-intensity for percolation, the percolation probability and the graph distance. Our results show that for urban areas, the Poisson Boolean Model gives a very good approximation, while for rural areas, the percolation probability stays far from 1 even far above the percolation threshold.
Modeling and Multi-objective Optimization of a Kind of Teaching Manipulator
Venue: arXiv cs.RO 2018
Year: 2018

A new kind of six degree-of-freedom teaching manipulator without actuators is designed, for recording and conveniently setting a trajectory of an industrial robot. The device requires good gravity balance and operating force performance to ensure being controlled easily and fluently. In this paper, we propose a process for modeling the manipulator and then the model is used to formulate a multi-objective optimization problem to optimize the design of the testing manipulator. Three objectives, including total mass of the device, gravity balancing and operating force performance are analyzed and defined. A popular non-dominated sorting genetic algorithm (NSGA-II-CDP) is used to solve the optimization problem. The obtained solutions all outperform the design of a human expert. To extract design knowledge, an innovization study is performed to establish meaningful implicit relationship between the objective space and the decision space, which can be reused by the designer in future design process.
Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data
Venue: arXiv cs.HC 2018
Year: 2018

Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
Shopping Mall Attraction and Social Mixing at a City Scale
Venue: arXiv cs.SI 2018
Year: 2018

The social inclusion aspects of shopping malls and their effects on our understanding of urban spaces have been a controversial argument largely discussed in the literature. Shopping malls offer an open, safe and democratic version of the public space. Many of their detractors suggest that malls target their customers in subtle ways, promoting social exclusion. In this work, we analyze whether malls offer opportunities for social mixing by analyzing the patterns of shopping mall visits in a large Latin-American city: Santiago de Chile. We use a large XDR (Data Detail Records) dataset from a telecommunication company to analyze the mobility of $387,152$ cell phones around $16$ large malls in Santiago de Chile during one month. We model the influx of people to malls in terms of a gravity model of mobility, and we are able to predict the customer profile distribution of each mall, explaining it in terms of mall location, the population distribution, and mall size. Then, we analyze the concept of social attraction, expressed as people from low and middle classes being attracted by malls that target high-income customers. We include a social attraction factor in our model and find that it is negligible in the process of choosing a mall. We observe that social mixing arises only in peripheral malls located farthest from the city center, which both low and middle class people visit. Using a co-visitation model we show that people tend to choose a restricted profile of malls according to their socio-economic status and their distance from the mall. We conclude that the potential for social mixing in malls could be capitalized by designing public policies regarding transportation and mobility.
Recursive Feature Generation for Knowledge-based Learning
Venue: arXiv cs.AI 2018
Year: 2018

When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. Given a feature, the algorithm defines a new learning task over its set of values, and uses the knowledge base to solve the constructed learning task. The resulting classifier is then used as a new feature for the original problem. We have applied our algorithm to the domain of text classification using large semantic knowledge bases. We have shown that the generated features significantly improve the performance of existing learning algorithms.
diagnoseIT: Expertengestützte automatische Diagnose von Performance-Probleme in Enterprise-Anwendungen (Abschlussbericht)
Venue: arXiv cs.SE 2018
Year: 2018

This is the final report of the collaborative research project diagnoseIT on expert-guided automatic diagnosis of performance problems in enterprise applications.
Improved Image Segmentation via Cost Minimization of Multiple Hypotheses
Venue: arXiv cs.CV 2018
Year: 2018

Image segmentation is an important component of many image understanding systems. It aims to group pixels in a spatially and perceptually coherent manner. Typically, these algorithms have a collection of parameters that control the degree of over-segmentation produced. It still remains a challenge to properly select such parameters for human-like perceptual grouping. In this work, we exploit the diversity of segments produced by different choices of parameters. We scan the segmentation parameter space and generate a collection of image segmentation hypotheses (from highly over-segmented to under-segmented). These are fed into a cost minimization framework that produces the final segmentation by selecting segments that: (1) better describe the natural contours of the image, and (2) are more stable and persistent among all the segmentation hypotheses. We compare our algorithm's performance with state-of-the-art algorithms, showing that we can achieve improved results. We also show that our framework is robust to the choice of segmentation kernel that produces the initial set of hypotheses.
Keyshuffling Attack for Persistent Early Code Execution in the Nintendo 3DS Secure Bootchain
Venue: arXiv cs.CR 2018
Year: 2018

We demonstrate an attack on the secure bootchain of the Nintendo 3DS in order to gain early code execution. The attack utilizes the block shuffling vulnerability of the ECB cipher mode to rearrange keys in the Nintendo 3DS's encrypted keystore. Because the shuffled keys will deterministically decrypt the encrypted firmware binary to incorrect plaintext data and execute it, and because the device's memory contents are kept between hard reboots, it is possible to reliably reach a branching instruction to a payload in memory. This payload, due to its execution by a privileged processor and its early execution, is able to extract the hash of hardware secrets necessary to decrypt the device's encrypted keystore and set up a persistent exploit of the system.
Cross-domain CNN for Hyperspectral Image Classification
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to effectively learn high-capacity Convolutional Neural Networks (CNNs). To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral datasets. The network also contains the non-shared portions designed to handle the dataset specific spectral characteristics and the associated classification tasks. Our approach is the first attempt to learn a CNN for multiple hyperspectral datasets, in an end-to-end fashion. Moreover, we have experimentally shown that the proposed network trained on three of the widely used datasets outperform all the baseline networks which are trained on single dataset.
The time geography of segregation during working hours
Venue: arXiv cs.SI 2018
Year: 2018

Understanding segregation is essential to develop planning tools for building more inclusive cities. Theoretically, segregation at the work place has been described as lower compared to residential segregation given the importance of skill complementarity among other productive factors shaping the economies of cities. This paper tackles segregation during working hours from a dynamical perspective by focusing on the movement of urbanites across the city. In contrast to measuring residential patterns of segregation, we used mobile phone data to infer home-work trajectory net- works and apply a community detection algorithm to the example city of Santiago, Chile. We then describe qualitatively and quantitatively outlined communities, in terms of their socio eco- nomic composition. We then evaluate segregation for each of these communities as the probability that a person from a specific community will interact with a co-worker from the same commu- nity. Finally, we compare these results with simulations where a new work location is set for each real user, following the empirical probability distributions of home-work distances and angles of direction for each community. Methodologically, this study shows that segregation during working hours for Santiago is unexpectedly high for most of the city with the exception of its central and business district. In fact, the only community that is not statistically segregated corresponds to the downtown area of Santiago, described as a zone of encounter and integration across the city.
A Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of Multinomials
Venue: arXiv cs.LG 2018
Year: 2018

Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural networks can provide more powerful mapping capability than the traditional feedforward neural networks (Sigma-Sigma neural networks). In the existing literature, in order to reduce the number of the Pi nodes in the Pi layer, a special multinomial P_s is used in SPSNNs. Each monomial in P_s is linear with respect to each particular variable sigma_i when the other variables are taken as constants. Therefore, the monomials like sigma_i^n or sigma_i^n sigma_j with n>1 are not included. This choice may be somehow intuitive, but is not necessarily the best. We propose in this paper a modified Sigma-Pi-Sigma neural network (MSPSNN) with an adaptive approach to find a better multinomial for a given problem. To elaborate, we start from a complete multinomial with a given order. Then we employ a regularization technique in the learning process for the given problem to reduce the number of monomials used in the multinomial, and end up with a new SPSNN involving the same number of monomials (= the number of nodes in the Pi-layer) as in P_s. Numerical experiments on some benchmark problems show that our MSPSNN behaves better than the traditional SPSNN with P_s.
Taking Control: Design and Implementation of Botnets for Cyber-Physical Attacks with CPSBot
Venue: arXiv cs.CR 2018
Year: 2018

Recently, botnets such as Mirai and Persirai targeted IoT devices on a large scale. We consider attacks by botnets on cyber-physical systems (CPS), which require advanced capabilities such as controlling the physical processes in real-time. Traditional botnets are not suitable for this goal mainly because they lack process control capabilities, are not optimized for low latency communication, and bots generally do not leverage local resources. We argue that such attacks would require cyber-physical botnets. A cyber-physical botnet needs coordinated and heterogeneous bots, capable of performing adversarial control strategies while subject to the constraints of the target CPS. In this work, we present CPSBot, a framework to build cyber-physical botnets. We present an example of a centralized CPSBot targeting a centrally controlled system and a decentralized CPSBot targeting a system distributed control. We implemented the former CPSBot using MQTT for the C&C channel and Modbus/TCP as the target network protocol and we used it to launch several attacks on real and simulated Water Distribution. We evaluate our implementation with distributed reply and distributed impersonation attacks on a CPS, and show that malicious control with negligible latency is possible.
Optimal LRC codes for all lenghts n <= q
Venue: arXiv cs.IT 2018
Year: 2018

A family of distance-optimal LRC codes from certain subcodes of $q$-ary Reed-Solomon codes, proposed by I.~Tamo and A.~Barg in 2014, assumes that the code length $n$ is a multiple of $r+1.$ By shortening codes from this family, we show that it is possible to lift this assumption, still obtaining distance-optimal codes.
Study of Uniform Jitter Mechanism for Metric-based Wireless Routing
Venue: arXiv cs.NI 2018
Year: 2018

Many wireless protocols wait a small and random amount of time which is called jitter before sending a packet to avoid high contention and packet collision. Jitter has been already proposed for many routing protocols including AODV and LOADng. However, since they do not consider any link quality parameters or metrics (such as ETX) in routing, they fail to be efficient in metric-based routing protocols. A metric-based jitter mechanism is proposed in this paper and a closed form expression is derived that enables us to obtain the probability of delay inversion for all jitter mechanisms available. Simulation results are also presented to show the performance of different jitter mechanisms.